Method and system for computer-aided triage of stroke

ABSTRACT

A system for computer-aided triage includes a router, a remote computing system, and a client application. A method for computer-aided triage includes receiving a data packet associated with a patient and taken at a point of care; checking for a suspected condition associated with the data packet; in an event that the suspected condition is detected, determining a recipient based on the suspected condition; and transmitting information to a device associated with the recipient.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/938,598, filed 24 Jul. 2020, which claims the benefit of U.S.Provisional Application No. 62/880,227, filed 30 Jul. 2019, each ofwhich is incorporated herein in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the medical diagnostic field, andmore specifically to a new and useful system and method forcomputer-aided triage in the medical diagnostic field.

BACKGROUND

In current triaging workflows, especially those in an emergency setting,a patient presents at a first point of care, where an assessment, suchas imaging, is performed. The image data is then sent to a standardradiology workflow, which typically involves: images (equivalentlyreferred to herein as instances) being uploaded to a radiologist'squeue, the radiologist reviewing the images at a workstation, theradiologist generating a report, an emergency department doctorreviewing the radiologist's report, the emergency department doctordetermining a specialist to contact, and making a decision of how totreat and/or transfer the patient to a 2^(nd) point of care. Thisworkflow is typically very time-consuming, which increases the time ittakes to treat and/or transfer a patient to a specialist. In manyconditions, especially those involving stroke, time is extremelysensitive, as it is estimated that in the case of stroke, a patientloses about 1.9 million neurons per minute that the stroke is leftuntreated (Saver et al.). Further, as time passes, the amount and typesof treatment options available to the patient decrease.

In some instances, such as that of brain tissue injury and/or death(e.g., ischemic core, ischemic penumbra, etc.), the time until treatmentis particularly critical, not only because the brain tissue continues toexperience greater injury/death as time passes, but because theparticular treatment option can be highly dependent upon the amount oftime that has passed, as this determines any or all of: an amount ofaffected brain, a severity of the affected brain, and a function of theaffected brain.

Thus, there is a need in the triaging field to create an improved anduseful system and method for decreasing the time it takes to determine asuspected condition and initiate treatment for the patient.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a system for computer-aided triage.

FIG. 2 is a schematic of a method for computer-aided triage.

FIG. 3 depicts a variation of a method for computer-aided triage.

FIG. 4 depicts a variation of a portion of a method for computer-aidedtriage.

FIGS. 5A and 5B depict a variation of an application on a user device.

FIG. 6 depicts a variation of a system for computer-aided triage.

FIG. 7 depicts a variation of a method for computer-aided triage.

FIG. 8 depicts a variation of the method.

FIG. 9 depicts a variation of the method involving recommending thepatient for a clinical trial.

FIG. 10 depicts a variation of a notification transmitted to a device ofa recipient.

FIG. 11 depicts a variation of a notification and subsequent workflow ofrecommending a patient for a clinical trial.

FIG. 12 depicts a variation of the system.

FIG. 13 depicts variations of a client application executing on firstand second user devices.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 1 , a system 100 for computer-aided triage includes arouter, a remote computing system, and a client application.Additionally or alternatively, the system 100 can include any number ofcomputing systems (e.g., local, remote), servers (e.g., PACS server),storage, lookup table, memory, and/or any other suitable components.Further additionally or alternatively, the system can include any or allof the components, embodiments, and examples as described in any or allof: U.S. application Ser. No. 16/012,458, filed 19 Jun. 2018; and U.S.application Ser. No. 16/012,495, filed 19 Jun. 2018; U.S. applicationSer. No. 16/688,721, filed 19Nov. 2019; and U.S. application Ser. No.16/913,754, filed 26 Jun. 2020; each of which is incorporated in itsentirety by this reference.

As shown in FIG. 2 , a method 200 for computer-aided triage includesreceiving a data packet associated with a patient and taken at a firstpoint of care S205; checking for a suspected condition associated withthe data packet S220; in an event that the suspected condition isdetected, determining a recipient based on the suspected condition S230;and transmitting information to a device associated with the recipientS250. Additionally or alternatively, the method 200 can include any orall of: transmitting data to a remote computing system S208; preparing adata packet for analysis S210; determining a parameter associated with adata packet; determining a treatment option based on the parameter;preparing a data packet for transfer S240; receiving an input from therecipient; initiating treatment of the patient; transmitting informationto a device associated with a second point of care; aggregating data;and/or any other suitable processes. Further additionally oralternatively, the method 200 can include any or all of the processes,embodiments, and examples described in any or all of: U.S. applicationSer. No. 16/012,458, filed 19 Jun. 2018; and U.S. application Ser. No.16/012,495, filed 19 Jun. 2018; U.S. application Ser. No. 16/688,721,filed 19 Nov. 2019; and U.S. application Ser. No. 16/913,754, filed 26Jun. 2020; each of which is incorporated in its entirety by thisreference, or any other suitable processes performed in any suitableorder. The method 200 can be performed with a system 100 as describedabove and/or any other suitable system.

2. Benefits

The system and method for computer-aided triage can confer severalbenefits over current systems and methods.

In a first variation, the system and/or method confers the benefit ofreducing the time to match and/or transfer a patient presenting with acondition (e.g., ischemia, ischemic core, ischemic penumbra, ischemicstroke, etc.) to a specialist. In a specific example, for instance, theaverage time between generating a non-contrast dataset and notifying aspecialist is reduced from over 50 minutes to less than 8 minutes.

In a second variation, additional or alternative to those describedabove, the system and/or method provides a parallel process to atraditional workflow (e.g., standard radiology workflow), which canconfer the benefit of reducing the time to determine a treatment optionwhile having the outcome of the traditional workflow as a backup in thecase that an inconclusive or inaccurate determination (e.g., falsenegative, false positive, etc.) results from the method.

In a third variation, additional or alternative to those describedabove, the system and/or method is configured to have a high sensitivity(e.g., 87.8%, approximately 88%, between 81% and 93%, greater than 87%,etc.), which functions to detect a high number of true positive casesand help these patients reach treatment faster. In the event that thisresults in a false positive, only a minor disturbance—if any—is causedto a specialist, which affects the specialist's workflow negligibly(e.g., less than 5 minutes), if at all. In a specific example, themethod is configured to have a sensitivity above a predeterminedthreshold after a particular category of artifacts (e.g., motionartifacts, metal artifacts, etc.) have been checked for.

In a fourth variation, additional or alternative to those describedabove, the system and/or method confers the benefit of minimizing theoccurrence of false positive cases (e.g., less than 10% occurrence, lessthan 5% occurrence, less than, which functions to minimize disturbancescaused to specialists or other individuals. This can function tominimize unnecessary disturbances to specialists in variations in whichspecialists or other users are notified on a mobile device upondetection of a potential brain condition (e.g., ischemic core), as itcan minimize the occurrence of a specialist being alerted (e.g.,potentially at inconvenient times of day, while the specialist isotherwise occupied, etc.) for false positives, while still maintaining afallback in the standard radiology workflow in the event that a truepositive is missed. In a set of specific examples, the method includestraining (e.g., iteratively training) a set of deep learning modelsinvolved in ischemic condition detection on images originally detectedto be a true positive but later identified as a false positive.

In a fifth variation, additional or alternative to those describedabove, the system and/or method confers the benefit of reorganizing aqueue of patients, wherein patients having a certain condition aredetected early and prioritized (e.g., moved to the front of the queue).

In a sixth variation, additional or alternative to those describedabove, the system and/or method confers the benefit of determining apatient condition (e.g., brain ischemia, ischemic core, ischemicpenumbra, dead brain volume above a predetermined threshold, etc.) witha non-contrast (e.g., non-perfusion) scan. In a specific example, thisdetermination is made in less time (e.g., after the non-contrast scanhas been performed, in absence of a non-contrast scan, etc.) than itwould take to perform and assess a contrast scan (e.g., CTP scan).

In a seventh variation, additional or alternative to those describedabove, the system and/or method confers the benefit of determiningactionable analytics to optimize a workflow, such as an emergency roomtriage workflow.

In an eighth variation, additional or alternative to those describedabove, the system and/or method confers the benefit of determining(e.g., quantifying) an amount of brain that can potentially be saved(e.g., is uninjured, is reversibly injured, etc.), which cansubsequently be used in future decision making (e.g., performing anoperation only when a percentage of brain above a predeterminedthreshold can be saved and/or maintained). Additionally oralternatively, the method can function to determine which areas of thebrain (e.g., functional regions, territories, lobes, etc.) can be saved(and/or are not able to saved), which can also be used in futuredecision making (e.g., anticipating and/or scheduling a particularrehabilitation, refraining from drastic intervention if a critical areaof the brain has been damaged, etc.).

In a ninth variation, additional or alternative to those describedabove, the system and/or method confers the benefit of detecting a brainischemic condition (e.g., ischemia, ischemic core, ischemic penumbra,ischemic stroke, etc.), which is conventionally hard to detect,especially in non-perfusion/non-contrast scans. In some examples, forinstance, the method leverages a natural symmetry of the brain betweenthe left and right halves to detect these subtle changes in anon-perfusion scan. In specific examples, a mirror image of each imageis taken and used as an input, along with the images (original images),to a machine learning model to identify the subtle, hard-to-detectdifferences corresponding to an ischemic condition (e.g., ischemic core,ischemic penumbra, ischemic stroke, etc.). This can have advantages overconventional attempts at solving this, which attempt to detect ischemicchanges through primarily thresholding the set of images based on aparticular pixel value threshold (e.g., a gray value), but variationsthroughout the brain (and throughout different patients and/or images)would either require a large amount of thresholds to be applied, ofteninaccurately, and/or the results would not be trustworthy.

In a tenth variation, additional or alternative to those describedabove, the system and/or method confers the benefit of automaticallydetermining a potential ischemia (e.g., ischemic core) and/or aparameter associated with the ischemia (e.g., percentage and/or amountof brain irreversibly damaged, brain damage severity, brain damagelocation, brain function corresponding to damaged region, etc.) in a setof non-contrast scans and notifying a specialist prior to theidentification of a potential brain ischemia by a radiologist during aparallel workflow. In conventional systems and methods, the contrastdistinction of contrast images is required to detect (e.g., accuratelydetect) ischemic conditions.

In an eleventh variation, additional or alternative to those describedabove, the system and/or method confers the benefit of recommending apatient for a clinical trial based on an automated processing of a setof images (e.g., brain images) associated with the patient.

Additionally or alternatively, the system and method can confer anyother benefit.

3. System

The system preferably interfaces with one or more points of care (e.g.,1^(st) point of care, 2^(nd) point of care, 3rd point of care, etc.),which are each typically a healthcare facility. A 1^(st) point of careherein refers to the healthcare facility at which a patient presents,typically where the patient first presents (e.g., in an emergencysetting). Conventionally, healthcare facilities include spokefacilities, which are often general (e.g., non-specialist, emergency,etc.) facilities, and hub (e.g., specialist) facilities, which can beequipped or better equipped (e.g., in comparison to spoke facilities)for certain procedures (e.g., mechanical thrombectomy), conditions(e.g., stroke), or patients (e.g., high risk). Patients typicallypresent to a spoke facility at a 1^(st) point of care, but canalternatively present to a hub facility, such as when it is evident whatcondition their symptoms reflect, when they have a prior history of aserious condition, when the condition has progressed to a high severity,when a hub facility is closest, randomly, or for any other reason. Ahealthcare facility can include any or all of: a hospital, clinic,ambulances, doctor's office, imaging center, laboratory, primary strokecenter (PSC), comprehensive stroke center (CSC), stroke ready center,interventional ready center, or any other suitable facility involved inpatient care and/or diagnostic testing.

A patient can be presenting with symptoms of a condition, no symptoms(e.g., presenting for routine testing), or for any other suitablesymptoms. In some variations, the patient is presenting with one or morestroke symptoms (e.g., vessel occlusion symptoms), such as, but notlimited to: weakness, numbness, speech abnormalities, and facialdrooping. Typically, these patients are then treated in accordance witha stroke protocol, which typically involves an imaging protocol at animaging modality, such as, but not limited to: a non-contrast CT (NCCT)scan of the head, a CTA scan of the head and neck, a CT perfusion (CTP)scan of the head.

A healthcare worker herein refers to any individual or entity associatedwith a healthcare facility, such as, but not limited to: a physician,emergency room physician (e.g., orders appropriate lab and imaging testsin accordance with a stroke protocol), radiologist (e.g., on-dutyradiologist, healthcare worker reviewing a completed imaging study,healthcare working authoring a final report, etc.), neuroradiologist,specialist (e.g., neurovascular specialist, vascular neurologist,neuro-interventional specialist, neuro-endovascular specialist,expert/specialist in a procedure such as mechanical thrombectomy,cardiac specialist, etc.), administrative assistant, healthcare facilityemployee (e.g., staff employee), emergency responder (e.g., emergencymedical technician), or any other suitable individual.

The image data can include computed tomography (CT) data (e.g.,radiographic CT, non-contrast CT, CT perfusion, etc.), preferablynon-contrast CT data (e.g., axial data, axial series of slices,consecutive slices, etc.) but can additionally or alternatively anyother suitable image data. The image data is preferably generated at animaging modality (e.g., scanner at the 1^(st) point of care), such as aCT scanner, magnetic resonance imaging (MRI) scanner, ultrasound system,or any other scanner. Additionally or alternatively, image data can begenerated from a camera, user device, accessed from a database orweb-based platform, drawn, sketched, or otherwise obtained.

In some variations, the image data includes a set of non-perfusion CTimages. While CT perfusion (CTP) images would likely make any braintissue injury and/or death (e.g., ischemic penumbra, ischemic core,etc.) much more visible than in non-perfusion images, the time requiredto perform a non-perfusion scan is significantly less, which can providemore and better options treatment options for the patient, as well asdecrease a total amount of irreversible tissue damage.

3.1 System—Router 110

The system 100 can include a router 110 (e.g., medical routing system),which functions to receive a data packet (e.g., dataset) includinginstances (e.g., images, scans, etc.) taken at an imaging modality(e.g., scanner) via a computing system (e.g., scanner, workstation, PACSserver) associated with a 1^(st) point of care. The instances arepreferably in the Digital Imaging and Communications in Medicine (DICOM)file format, as well as generated and transferred between computingsystem in accordance with a DICOM protocol, but can additionally oralternatively be in any suitable format. Additionally or alternatively,the instances can include any suitable medical data (e.g., diagnosticdata, patient data, patient history, patient demographic information,etc.), such as, but not limited to, PACS data, Health-Level 7 (HL7)data, electronic health record (EHR) data, or any other suitable data,and to forward the data to a remote computing system.

The instances preferably include (e.g., are tagged with) and/orassociated with a set of metadata, but can additionally or alternativelyinclude multiple sets of metadata, no metadata, extracted (e.g.,removed) metadata (e.g., for regulatory purposes, HIPAA compliance,etc.), altered (e.g., encrypted, decrypted, etc.) metadata, or any othersuitable metadata, tags, identifiers, or other suitable information.

The router 110 can refer to or include a virtual entity (e.g., virtualmachine, virtual server, etc.) and/or a physical entity (e.g., localserver). The router can be local (e.g., at a 1^(st) healthcare facility,2^(nd) healthcare facility, etc.) and associated with (e.g., connectedto) any or all of: on-site server associated with any or all of theimaging modality, the healthcare facility's PACS architecture (e.g.,server associated with physician workstations), or any other suitablelocal server or DICOM compatible device(s). Additionally oralternatively, the router can be remote (e.g., locate at a remotefacility, remote server, cloud computing system, etc.), and associatedwith any or all of: a remote server associated with the PACS system, amodality, or another DICOM compatible device such as a DICOM router.

The router 110 preferably operates on (e.g., is integrated into) asystem (e.g., computing system, workstation, server, PACS server,imaging modality, scanner, etc.) at a 1^(st) point of care butadditionally or alternatively, at a 2^(nd) point of care, remote server(e.g., physical, virtual, etc.) associated with one or both of the1^(st) point of care and the 2^(nd) point of care (e.g., PACS server,EHR server, HL7 server), a data storage system (e.g., patient records),or any other suitable system. In some variations, the system that therouter operates on is physical (e.g., physical workstation, imagingmodality, scanner, etc.) but can additionally or alternatively includevirtual components (e.g., virtual server, virtual database, cloudcomputing system, etc.).

The router no is preferably configured to receive data (e.g., instances,images, study, series, etc.) from an imaging modality, preferably animaging modality (e.g., CT scanner, MRI scanner, ultrasound machine,etc.) at a first point of care (e.g., spoke, hub, etc.) but canadditionally or alternatively be at a second point of care (e.g., hub,spoke, etc.), multiple points of care, or any other healthcare facility.The router can be coupled in any suitable way (e.g., wired connection,wireless connection, etc.) to the imaging modality (e.g., directlyconnected, indirectly connected via a PACS server, etc.). Additionallyor alternatively, the router 100 can be connected to the healthcarefacility's PACS architecture, or other server or DICOM-compatible deviceof any point of care or healthcare facility.

In some variations, the router includes a virtual machine operating on acomputing system (e.g., computer, workstation, user device, etc.),imaging modality (e.g., scanner), server (e.g., PACS server, server at1^(st) healthcare facility, server at 2^(nd) healthcare facility, etc.),or other system. In a specific example, the router is part of a virtualmachine server. In another specific example, the router is part of alocal server.

3.2 System—Remote Computing System 120

The system 100 can include a remote computing system 120, which canfunction to receive and process data packets (e.g., dataset fromrouter), determine a treatment option (e.g., select a 2^(nd) point ofcare, select a specialist, etc.), interface with a user device (e.g.,mobile device), compress a data packet, extract and/or remove metadatafrom a data packet (e.g., to comply with a regulatory agency), orperform any other suitable function.

Preferably, part of the method 200 is performed at the remote computingsystem (e.g., cloud-based), but additionally or alternatively all of themethod 200 can be performed at the remote computing system, the method200 can be performed at a local computing system (e.g., at a healthcarefacility), any other suitable computing system(s). In some variations,the remote computing system 120 provides an interface for technicalsupport (e.g., for a client application) and/or analytics. In somevariations, the remote computing system includes storage and isconfigured to store and/or access a lookup table, wherein the lookuptable functions to determine a treatment option (e.g., 2^(nd) point ofcare), a contact associated with the 2^(nd) point of care, and/or anyother suitable information.

In some variations, the remote computing system 120 connects multiplehealthcare facilities (e.g., through a client application, through amessaging platform, etc.).

In some variations, the remote computing system 120 functions to receiveone or more inputs and/or to monitor a set of client applications (e.g.,executing on user devices, executing on workstations, etc.).

3.3 System—Application 130

The system 100 can include one or more applications 130 (e.g., clients,client applications, client application executing on a device, etc.),such as the application shown in FIGS. 5A and 5B, which individually orcollectively function to provide one or more outputs (e.g., from theremote computing system) to a contact. Additionally or alternatively,the applications can individually or collectively function to receiveone or more inputs from a contact, provide one or more outputs to ahealthcare facility (e.g., first point of care, second point of care,etc.), establish communication between healthcare facilities, or performany other suitable function.

In some variations, one or more features of the application (e.g.,appearance, information content, information displayed, user interface,graphical user interface, etc.) are determined based on any or all of:the type of device that the application is operating on (e.g., userdevice vs. healthcare facility device, mobile device vs. stationarydevice), where the device is located (e.g., 1^(st) point of care, 2^(nd)point of care, etc.), who is interacting with the application (e.g.,user identifier, user security clearance, user permission, etc.), or anyother characteristic. In some variations, for instance, an applicationexecuting on a healthcare facility will display a 1^(st) set ofinformation (e.g., uncompressed images, metadata, etc.) while anapplication executing on a user device will display a 2^(nd) set ofinformation (e.g., compressed images, no metadata, etc.). In somevariations, the type of data to display is determined based on any orall of: an application identifier, mobile device identifier, workstationidentifier, or any other suitable identifier.

The outputs of the application can include any or all of: an alert ornotification (e.g., push notification, text message, call, email, etc.);an image set (e.g., compressed version of images taken at scanner,preview of images taken at scanner, images taken at scanner, etc.); aset of tools for interacting with the image set, such as any or all ofpanning, zooming, rotating, window leveling, scrolling, maximumintensity projection [MIP] (e.g., option to select the slab thickness ofa MIP); changing the orientation of 3D scan (e.g., changing betweenaxial, coronal, and sagittal views), showing multiple views of a set ofimages; a worklist (e.g., list of patients presenting for and/orrequiring care, patients being taken care of by specialist, patientsrecommended to specialist, procedures to be performed by specialist,etc.); a messaging platform (e.g., HIPAA-compliant messaging platform,texting platform, video messaging, etc.); a telecommunication platform(e.g., video conferencing platform); a directory of contact information(e.g., 1^(st) point of care contact info, 2^(nd) point of care contactinfo, etc.); tracking of a workflow or activity (e.g., real-time or nearreal-time updates of patient status/workflow/etc.); analytics based onor related to the tracking (e.g., predictive analytics such as predictedtime remaining in radiology workflow or predicted time until strokereaches a certain severity; average time in a workflow, average time totransition to a second point of care, etc.); or any other suitableoutput.

The inputs can include any or all of the outputs described previously,touch inputs (e.g., received at a touch-sensitive surface), audioinputs, optical inputs, or any other suitable input. The set of inputspreferably includes an input indicating receipt of an output by acontact. This can include an active input from the contact (e.g.,contact makes selection at application), a passive input (e.g., readreceipt), or any other input.

In one variation, the system 100 includes a mobile device application130 and a workstation application 130—both connected to the remotecomputing system—wherein a shared user identifier (e.g., specialistaccount, user account, etc.) can be used to connect the applications(e.g., retrieve a case, image set, etc.) and determine the informationto be displayed at each application (e.g., variations of imagedatasets). In one example, the information to be displayed (e.g.,compressed images, high-resolution images, etc.) can be determined basedon: the system type (e.g., mobile device, workstation), the applicationtype (e.g., mobile device application, workstation application,), theuser account (e.g., permissions, etc.), any other suitable information,or otherwise determined.

The application can include any suitable algorithms or processes foranalysis, and part or all of the method 200 can be performed by aprocessor associated with the application.

The application preferably includes both front-end (e.g., applicationexecuting on a user device, application executing on a workstation,etc.) and back-end components (e.g., software, processing at a remotecomputing system, etc.), but can additionally or alternatively includejust front-end or back-end components, or any number of componentsimplemented at any suitable system(s).

3.4 System—Additional Components

The system 100 and/or or any component of the system 100 can optionallyinclude or be coupled to any suitable component for operation, such as,but not limited to: a processing module (e.g., processor,microprocessor, etc.), control module (e.g., controller,microcontroller), power module (e.g., power source, battery,rechargeable battery, mains power, inductive charger, etc.), sensorsystem (e.g., optical sensor, camera, microphone, motion sensor,location sensor, etc.), or any other suitable component.

3.5 System—Variations

In one variation, the system includes a router 110, which operates at acomputing system at a 1^(st) point of care and receives image data froman imaging modality. The router transmits the image data to a remotecomputing system, wherein a series of algorithms (e.g., machine learningalgorithms) are performed at the remote computing system, whichdetermines a hypothesis for whether or not a suspected condition (e.g.,ischemic core) is present based on the image data and/or any associatedmetadata. Based on the determination, a contact is determined from alookup table (e.g., in storage at the remote computing system), whereinthe contact is notified at a user device (e.g., personal device) andsent image data through a client application executing on the userdevice. One or more inputs from the contact at the application can bereceived at the remote computing system, which can be used to determinea next point of care for the patient.

Additionally or alternatively, any or all of the computing can beperformed at a local computing system (e.g., at the 1^(st) point ofcare), a computing system associated with a user device, and/or anyother suitable computing system.

4. Method

As shown in FIG. 2 , a method 200 for computer-aided triage includesreceiving a data packet associated with a patient and taken at a firstpoint of care S205; checking for a suspected condition associated withthe data packet S220; in an event that the suspected condition isdetected, determining a recipient based on the suspected condition S230;and transmitting information to a device associated with the recipientS250. Additionally or alternatively, the method 200 can include any orall of: transmitting data to a remote computing system S208; preparing adata packet for analysis S210; determining a parameter associated with adata packet; determining a treatment option based on the parameter;preparing a data packet for transfer S240; receiving an input from therecipient; initiating treatment of the patient; transmitting informationto a device associated with a second point of care; aggregating data;and/or any other suitable processes. Further additionally oralternatively, the method 200 can include any or all of the processes,embodiments, and examples described in any or all of: U.S. applicationSer. No. 16/012,458, filed 19 Jun. 2018; and U.S. application Ser. No.16/012,495, filed 19 Jun. 2018; U.S. application Ser. No. 16/688,721,filed 19 Nov. 2019; and U.S. application Ser. No. 16/913,754, filed 26Jun. 2020; each of which is incorporated in its entirety by thisreference, or any other suitable processes performed in any suitableorder. The method 200 can be performed with a system 100 as describedabove and/or any other suitable system.

The method 200 can optionally be performed separate from but in parallelwith (e.g., contemporaneously with, concurrently with, etc.) a standardradiology workflow (e.g., as shown in FIG. 3 ), but can additionally oralternatively be implemented within a standard workflow, be performed ata separate time with respect to a standard workflow, or be performed atany suitable time.

The method 200 can be partially or fully implemented with the system 100or with any other suitable system.

The method 200 functions to improve communication across healthcarefacility networks (e.g., stroke networks, spokes and hubs, etc.) anddecrease the time required to transfer a patient having a suspectedtime-sensitive condition (e.g., brain condition, stroke, ischemicpenumbra, ischemic core, hemorrhagic stroke, ischemic stroke, largevessel occlusion (LVO), cardiac event, trauma, etc.) from a first pointof care (e.g., spoke, non-specialist facility, stroke center, ambulance,etc.) to a second point of care (e.g., hub, specialist facility,comprehensive stroke center, etc.), wherein the second point of carerefers to a healthcare facility equipped to treat the patient. In somevariations, the second point of care is the first point of care, whereinthe patient is treated at the healthcare facility to which he or sheinitially presents.

The method 200 can optionally function as a parallel workflow tool,wherein the parallel workflow is performed contemporaneously with (e.g.,concurrently, during, partially during) a standard radiology workflow(e.g., radiologist queue), but can additionally or alternatively beimplemented within a standard workflow (e.g., to automate part of astandard workflow process, decrease the time required to perform astandard workflow process, etc.), be performed during a workflow otherthan a radiology workflow (e.g., during a routine examination workflow),or at any other suitable time.

The method 200 is preferably performed in response to a patientpresenting at a first point of care. The first point of care can be anemergency setting (e.g., emergency room, ambulance, imaging center,etc.), equivalently referred to herein as an acute setting, or anysuitable healthcare facility, such as those described previously. Thepatient is typically presenting with (or suspected to be presentingwith) a time-sensitive condition, such as a neurovascular condition(e.g., stroke, brain tissue injury, brain tissue death, ischemic stroke,ischemia, ischemic core, ischemic penumbra, occlusion, large vesselocclusion (LVO), thrombus, aneurysm, etc.), cardiac event or condition(e.g., cardiovascular condition, heart attack, etc.), trauma (e.g.,acute trauma, blood loss, etc.), or any other time-sensitive (e.g.,life-threatening) condition. In other variations, the method isperformed for a patient presenting to a routine healthcare setting(e.g., non-emergency setting, clinic, imaging center, etc.), such as forroutine testing, screening, diagnostics, imaging, clinic review,laboratory testing (e.g., blood tests), or for any other reason.

Any or all of the method can be performed using any number of machinelearning (e.g., deep learning) modules. Each module can utilize one ormore of: supervised learning (e.g., using logistic regression, usingback propagation neural networks, using random forests, decision trees,etc.), unsupervised learning (e.g., using an Apriori algorithm, usingK-means clustering), semi-supervised learning, reinforcement learning(e.g., using a Q-learning algorithm, using temporal differencelearning), and any other suitable learning style. Each module of theplurality can implement any one or more of: a regression algorithm(e.g., ordinary least squares, logistic regression, stepwise regression,multivariate adaptive regression splines, locally estimated scatterplotsmoothing, etc.), an instance-based method (e.g., k-nearest neighbor,learning vector quantization, self-organizing map, etc.), aregularization method (e.g., ridge regression, least absolute shrinkageand selection operator, elastic net, etc.), a decision tree learningmethod (e.g., classification and regression tree, iterative dichotomiser3, C4.5, chi-squared automatic interaction detection, decision stump,random forest, multivariate adaptive regression splines, gradientboosting machines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, boostrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), V-nets, U-nets, and/or any suitable form of machine learningalgorithm. Each module can additionally or alternatively be a:probabilistic module, heuristic module, deterministic module, or be anyother suitable module leveraging any other suitable computation method,machine learning method, or combination thereof.

In preferred variations, a set of U-nets and/or V-nets are used in asegmentation process to identify and isolate brain matter correspondingto a suspected patient condition (e.g., ischemic core, ischemicpenumbra, ischemic stroke, etc.).

Each module can be validated, verified, reinforced, calibrated, orotherwise updated based on newly received, up-to-date measurements; pastmeasurements recorded during the operating session; historicmeasurements recorded during past operating sessions; or be updatedbased on any other suitable data. Each module can be run or updated:once; at a predetermined frequency; every time the method is performed;every time an unanticipated measurement value is received; or at anyother suitable frequency. The set of modules can be run or updatedconcurrently with one or more other modules, serially, at varyingfrequencies, or at any other suitable time. Each module can bevalidated, verified, reinforced, calibrated, or otherwise updated basedon newly received, up-to-date data; past data or be updated based on anyother suitable data. Each module can be run or updated: in response todetermination of an actual result differing from an expected result; orat any other suitable frequency.

4.1 Method—Receiving a Data Packet Associated with a Patient and Takenat a First Point of Care S205

The method 200 includes receiving data (e.g., data packet) associatedwith a patient and taken at a first point of care S205, which functionsto collect data relevant to assessing a patient condition.

The data is preferably received at a router 110, wherein the router isin the form of a virtual machine operating on a computing system (e.g.,computer, workstation, quality assurance (QA) workstation, readingworkstation, PACS server, etc.) coupled to or part of an imagingmodality (e.g., CT scanner, MRI scanner, etc.), or any other suitablerouter. Additionally or alternatively, data can be received at a remotecomputing system (e.g., from an imaging modality, from a database, aserver such as a PACS server, an internet search, social media, etc.),or at any other suitable computing system (e.g., server) or storage site(e.g., database). In some variations, for instance, a subset of the data(e.g., image data) is received at the router while another subset of thedata (e.g., patient information, patient history, etc.) is received at aremote computing system. In a specific example, the data subset receivedat the router is eventually transmitted to the remote computing systemfor analysis.

S205 is preferably performed in response to (e.g., after, in real timewith, substantially in real time with, with a predetermined delay, witha delay of less than 10 seconds, with a delay of less than 1 minute, atthe prompting of a medical professional, etc.) the data (e.g., each of aset of instances) being generated at the imaging modality. Additionallyor alternatively, S205 can be performed in response to a set of multipleinstances being generated by the imaging modality (e.g., after a partialseries has been generated, after a full series has been generated, aftera study has been generated, etc.), in response to a metadata tag beinggenerated (e.g., for an instance, for a series, for a study, etc.), inresponse to a trigger (e.g., request for images), throughout the method(e.g., as a patient's medical records are accessed, as information isentered a server, as information is retrieved from a server, etc.), orat any other suitable time.

S205 can be performed a single time or multiple times (e.g.,sequentially, at different times in the method, once patient conditionhas progressed, etc.). In one variation, each instance is received(e.g., at a router, at a remote computing system, etc.) individually asit is generated. In a second variation, a set of multiple instances(e.g., multiple images, full series, etc.) are received together (e.g.,after a scan has completed, after a particular anatomical component hasbeen imaged, etc.).

The router (e.g., virtual machine, virtual server, application runningon the image sampling system or a computing system connected to theimage sampling system, etc.) can be continuously ‘listening’ (e.g.,operating in a scanning mode, receiving mode, coupled to or include aradio operating in a suitable mode, etc.) for information from theimaging modality, can receive information in response to prompting of ahealthcare facility worker, in response to a particular scan type beinginitiated (e.g., in response to a head CTA scan being initiated), or inresponse to any other suitable trigger.

Image data is preferably received at the router (e.g., directly,indirectly, etc.) from the imaging modality (e.g., scanner) at which thedata was generated. Additionally or alternatively, image data or anyother data can be received from any computing system associated with thehealthcare facility's PACS server, any DICOM-compatible devices such asa DICOM router, or any other suitable computing system. The image datais preferably in the DICOM format but can additionally or alternativelyinclude any other data format.

In addition to or alternative to image data, the data can include blooddata, electronic medical record (EMR) data, unstructured EMR data,health level 7 (HL7) data, HL7 messages, clinical notes, or any othersuitable data related to a patient's medical state, condition, ormedical history.

The data preferably includes image data including a set of one or moreinstances (e.g., images), which can be unorganized, organized (e.g.,into a series, into a study, a sequential set of instances based oninstance creation time, acquisition time, image position, instancenumber, unique identification (UID), other acquisition parameters ormetadata tags, anatomical feature or location within body, etc.),complete, incomplete, randomly arranged, or otherwise arranged.

The image data is preferably received from (imaged at) a scanner. Thescanner can include any or all of: a computed tomography (CT) scanner, amagnetic resonance imaging (MRI) scanner, an ultrasound system, an X-Rayscanner, and/or any other suitable imaging device (e.g., camera). Theset of images can include any or all of: CT images (e.g., non-contrastCT [NCCT] images, contrast CT images, CT angiography images, etc.); MRIimages; X-ray images; ultrasound images; and/or any other suitableimages produced by any or all of these imaging devices or others.

In preferred variations for detecting ischemic core, the set of imagespreferably includes NCCT images of the patient's head.

Each instance preferably includes (e.g., is tagged with) a set ofmetadata associated with the image dataset, such as, but not limited to:one or more patient identifiers (e.g., name, identification number, UID,etc.), patient demographic information (e.g., age, race, sex, etc.),reason for presentation (e.g. presenting symptoms, medical severityscore, etc.), patient history (e.g., prior scans, prior diagnosis, priormedical encounters, etc.), medical record (e.g. history of presentillness, past medical history, allergies, medications, family history,social history, etc.), scan information, scan time, scan type (e.g.,anatomical region being scanned, scanning modality, scanner identifier,etc.), number of images in scan, parameters related to scan acquisition(e.g., timestamps, dosage, gurney position, scanning protocol, contrastbolus protocol, etc.), image characteristics (e.g., slice thickness,instance number and positions, pixel spacing, total number of slices,etc.), or any other suitable information.

In some variations, any or all of the data (e.g., image data) is taggedwith metadata associated with the standard DICOM protocol.

In some variations, one or more tags is generated and/or applied to thedata after the data is generated at an imaging modality. In someexamples, the tag is an identifier associated with the 1^(st) point ofcare (e.g., 1^(st) point of care location, imaging modality identifier,etc.), which can be retrieved by a 2^(nd) point of care in order tolocate the patient (e.g., to enable a quick transfer of the patient, toinform a specialist of who to contact or where to reach the patient,etc.).

Additionally or alternatively, image data can be received withoutassociated metadata (e.g., metadata identified later in the method,dataset privately tagged later with metadata later in the method, etc.).

In a first set of variations for detecting an ischemic condition (e.g.,ischemic core, ischemic penumbra, ischemic stroke, etc.), metadata ofimages are checked for metadata inclusion criteria, including/indicatingany or all of: a CT scan of the head, a non-contrast CT scan of thehead, an axial series, a slice thickness within a supported range, theabsence of missing slices, aligned instance numbers and positions,patient age above a minimum threshold (e.g., between 0 and 10, between10 and 20, between 20 and 30, above 30, etc.), consistent pixel spacing,a total number of slices within a predetermined range (e.g., between 18and 25), and/or any other suitable metadata inclusion criteria.

Data can be received (e.g., at the router) through a wired connection(e.g., local area network (LAN) connection), wireless connection, orthrough any combination of connections and information pathways.

In a first variation (e.g., for a patient presenting with symptoms of anischemic stroke, ischemic core, ischemic penumbra, etc.), S205 includesreceiving a non-perfusion CT (NCCT) scan of a patient's head. In apreferred specific example of this variation, the non-perfusion CT scanincludes an axial series of image instances (equivalently referred toherein as slices), each of the slices corresponding to an axialthickness between 2.5 and 5 millimeters, wherein the axial seriesincludes no missing slices and wherein the slices are properly ordered(e.g., instance numbers and positions aligned).

4.2 Method—Transmitting Data to a Remote Computing System S208

The method can include transmitting data to a remote computing system(e.g., remote server, PACS server, etc.) S208, which functions to enableremote processing of the data, robust process, or fast processing (e.g.,faster than analysis done in clinical workflow, faster than done in astandard radiology workflow, processing less than 20 minutes, less than10 minutes, less than 7 minutes, etc.) of the dataset.

The data (e.g., image data, image data and metadata, etc.) is preferablytransmitted to a remote computing system from a router (e.g., virtualmachine operating on a scanner) connected to a computing system (e.g.,scanner, workstation, PACS server, etc.) associated with a healthcarefacility, further preferably where the patient first presents (e.g.,1^(st) point of care), but can additionally or alternatively betransmitted from any healthcare facility, computing system, or storagesite (e.g., database).

Each instance (e.g., image) of the dataset (e.g., image dataset) ispreferably sent individually as it is generated at an imaging modalityand/or received at a router, but additionally or alternatively, multipleinstances can be sent together after a predetermined set (e.g., series,study, etc.) has been generated, after a predetermined interval of timehas passed (e.g., instances sent every 10 seconds), upon the promptingof a medical professional, or at any other suitable time. Furtheradditionally or alternatively, the order in which instances are sent toa remote computing system can depend one or more properties of thoseinstances (e.g., metadata). S208 can be performed a single time ormultiple times (e.g., after each instance is generated).

S208 can include transmitting all of the dataset (e.g., image datasetand metadata), a portion of the dataset (e.g., only image dataset,subset of image dataset and metadata, etc.), or any other information oradditional information (e.g., supplementary information such assupplementary user information).

The data is preferably transmitted through a secure channel, furtherpreferably through a channel providing error correction (e.g., overTCP/IP stack of 1st point of care), but can alternatively be sentthrough any suitable channel.

S208 can include any number of suitable sub-steps performed prior to orduring the transmitting of data to the remote computing system. Thesesub-steps can include any or all of: encrypting any or all of thedataset (e.g., patient information) prior to transmitting to the remotecomputing system, removing information (e.g., sensitive information),supplementing the dataset with additional information (e.g.,supplementary patient information, supplemental series of a study,etc.), compressing any or all of the dataset, or performing any othersuitable process.

4.3 Method—Preparing a Data Packet for Analysis S210

The method 200 preferably includes preparing a data packet S210 foranalysis (e.g., pre-processing), which can function to pre-process anyor all of the data packet, eliminate an irrelevant (e.g., incorrectlylabeled, irrelevant anatomical region, etc.) or unsuitable data packetfrom further analysis, produce one or more inputs for future processesin the method (e.g., an overlaid registered image), or perform any othersuitable function.

S210 can optionally include resizing one or more of the set ofinstances, which can function to reduce an amount of data beingprocessed in future processes. Additionally, resizing can function toshorten the time of transfer of data between points of the system (e.g.,from a virtual machine to a remote computing system, from a remotecomputing system to a mobile device and/or workstation, etc.), andtherefore subsequently reduce a total time required to process the data.Resizing the set of instances can be performed in accordance with (e.g.,as part of, contemporaneously with, based on the results of, etc.) oneor more registration processes, such as—but not limited to—any or all ofthe registration processes described below (e.g., a first set ofregistration processes, a second set of registration processes, etc.).Additionally or alternatively, the set of instances can be resized toparticular image size (e.g., predetermined image size, dynamicallydetermined image size, through downsampling, through losslesscompression, through lossy compression, etc.). In one variation, forinstance, S210 includes resizing each of the set of instances from512×512 to 256×256 or from 512×512 to 96×96.

Additionally or alternatively, S210 can include cropping one or more ofthe set of images (e.g., according to a predetermined crop size, toemphasize a particularly relevant region, to remove an unwanted region,to increase a uniformity of the region shown in a set of images, etc.),straightening out one or more of the set of images (e.g., rotating aportion of the image, translating a portion of the image, by registeringeach of a set of images with its mirror image, etc.), or otherwisepre-processing the set of images.

S210 can optionally include one or more registration processes (e.g.,image registration processes), which function to align, scale,calibrate, or otherwise adjust the set of images. Alternatively, theimages can proceed to future processes in absence of any registrationprocesses. The registration process(es) can be performed throughregistering any or all of the set of images (e.g., soft matter regionsextracted from set of images) to a reference set of images (e.g.,reference series); additionally or alternatively, any or all of theregistration process(es) can be performed in absence of comparing thepresent set of instances with a reference set of instances (e.g., bycomparing a first region of one instance with a second region of thesame instance, by comparing a right brain hemisphere of the instancewith a left brain hemisphere of the same instance, by comparing oneinstance of the set of images with another instance of the set ofimages, by comparing an instance with a mirror image of itself, etc.).

The registration can optionally be performed in response to a datapacket being filtered through a set of exclusion criteria. Additionallyor alternatively, registration can be performed prior to or in absenceof filtering, multiple times throughout the method, prior to filteringthrough a set of exclusion criteria, or at any other suitable time.

S210 can optionally include a first type of registration process,equivalently referred to herein as reference registration, wherein theset of images is registered against a reference set of images, whichfunctions to align and/or scale the set of images. Additionally, thisfirst registration process can function to identify a left and righthemisphere of the brain. The first registration process can be performedin response to receiving the set of images (e.g., prior to checking fora particular brain condition, prior to a second set of registrationprocesses, etc.), after a set of exclusion criteria have been checked orin response to any other process, and/or at any other suitable time.Alternatively, the method can be performed in absence of a first set ofregistration processes.

The reference registration can be any or all of: intensity-based,feature-based, or otherwise configured, and can include any or all of:point-mapping, feature-mapping, or any other suitable process. Thereference set of instances is preferably determined from a training setbut can alternatively be determined from any suitable dataset,computer-generated, or otherwise determined. The reference series can beselected for any or all of orientation, size, three-dimensionalpositioning, clarity, contrast, or any other feature. In one variation,a references series is selected from a training set, wherein thereference series is selected based on a feature parameter (e.g., largestfeature size such as largest skull dimension, smallest feature size,etc.) and/or a degree of alignment (e.g., maximal alignment, alignmentabove a predetermined threshold, etc.). Additionally or alternatively,any other criteria can be used to determine the reference series, thereference series can be randomly selected, formed from aggregatingmultiple sets of instances (e.g., multiple patient series), ordetermined in any other suitable way. In some variations, theregistration is performed with one or more particular software packages(e.g., SimpleElastix). In a specific example, the registration isperformed through affine registration (e.g., in SimpleElastix) with aset of predetermined (e.g., default) parameters and iterations (e.g.,4096 iterations, between 3000 and 5000 iterations, above 1000iterations, above 100 iterations, etc.) in each registration step.

S210 preferably includes a second type of registration processes,equivalently referred to herein as intra-image registration, whichfunctions to enable a comparison between multiple parts of a singleimage. This can enable, for instance, a comparison between a right brainhemisphere (e.g., in size, position, angle, etc.) and a left brainhemisphere from the same image instance. This can be used (e.g., at alater process in the method) to compare (e.g., map to) an area ofinjured tissue (e.g., ischemic penumbra, ischemic core, etc.) in a firstbrain region with a corresponding area in a second brain region (e.g., amirror image region, an opposing region, etc.). In a first variation,for instance, for a set of one or more images indicating a potentialischemia in a left hemisphere, the area of injured tissue in the lefthemisphere can be compared with a corresponding tissue region (e.g.,horizontally opposing in an aligned brain image) in a right hemisphere(e.g., to determine if there is injured tissue, to confirm that there isinjured tissue, to quantify or otherwise assess an amount of injuredtissue, etc.). In a second variation, for instance, for a set of one ormore images indicating a potential ischemia in a right hemisphere, thearea of injured tissue in the right hemisphere can be compared with acorresponding tissue region (e.g., non-injured region) in the lefthemisphere.

In preferred variations (e.g., as shown in FIG. 4 ), a registrationprocess includes producing a second input for analysis, which caninclude any or all of: a mirror image of the each of the set of images(e.g., after the set of images have been registered with a first set ofregistration processes, the original set of images, etc.), an overlaidimage including the image superimposed with (and optionally alignedwith) its mirror image, a registered mirror image (e.g., taken as amirrored version of the registered original image). The mirror image ispreferably received concurrently (e.g., at the same time, at anoverlapping time, immediately after, immediately before, etc.) with itscorresponding non-mirror image (e.g., original image, registeredoriginal image, etc.) to on or more deep learning models, but canadditionally or alternatively be received at different times (e.g.,after). Having mirror images for comparison can ultimately function toenable subtle visual differences (e.g., a color difference below apredetermined threshold, a sulcal dimension difference [such as adifference in spacing between adjacent sulci] below a predeterminedthreshold, a “texture” difference below a predetermined threshold, etc.)between regions of the image (e.g., between corresponding regions in theleft and right hemispheres) to be identified (e.g., with non-contrastscans). In these variations, for instance, a machine learning model canbe trained and/or used to better detect subtle changes in brain matter(e.g., from non-contrast images) corresponding to an ischemic conditionby receiving mirror images in addition to the set of images, whicheffectively enables ratios in contrast between corresponding left andright voxels to be utilized to determine ischemic regions. In specificexamples, the mirror images are overlaid and aligned with thecorresponding original images, wherein the original set of images andoverlaid images are received as an input to the machine learningmodel(s).

Additionally or alternatively, and other suitable input(s) can bereceived

A mirror image of each image is preferably formed by taking a mirrorimage about a central axis (e.g., y-axis as shown in FIG. 4 ) of theimage (e.g., prior to a first set of registrations steps, in absence ofa first set of registration steps, after the set of images has beenstraightened out, in absence of the set of images being straightenedout, etc.). Additionally or alternatively, a mirror image can be takenabout an anatomical line (e.g., cerebral fissure) or any other suitableaxis of the set of images. In some variations, a mirror image isproduced based on an unregistered original image. In additional oralterative variations, a first mirror image is produced, which is usedto register the original image, and then the mirror image is updated tobe registered based on the registered original image (e.g., formed fromthe mirror image of the registered image). In specific examples, theregistered mirror image is then overlaid with the registered originalimage. Additionally or alternatively, the original image and its mirrorimage can be registered together, not registered, not overlaid, and/orotherwise produced and/or modified.

Each image is preferably then superimposed with its mirror image,forming a superimposed image (equivalently referred to herein as theoverlaid image). The superimposed image can then be registered to alignthe image with its mirror image in the superimposed image, forming aregistered superimposed image (equivalently referred to herein as theregistered overlaid image). This functions to align the left and righthemispheres, enable a mapping of a region in a first brain hemisphere toa corresponding region in the opposing hemisphere, straighten theoverall scan, resize one or more regions of the scan (e.g., to correctfor a nonzero z-angle of the patient in the scanner), and/or perform anyother function. Alternatively, the image and its mirror image can remainseparate (e.g., and be processed independently).

Additional or alternative to variations in which a mirror image isproduced for each image, an intra-image registration process can includeseparating (e.g., dividing) each of the set of images (e.g., after afirst set of image registration processes, in absence of a first set ofimage registration processes, etc.) into multiple parts, such as theleft and right hemispheres (e.g., separating each image along a cerebralfissure). The hemispheres can be identified through any or all of: afirst set of registration steps, a segmentation process, a windowingprocess (e.g., based on Hounsfield unit values), a geometrical divisionof each image (e.g., division along a centerline), or any other suitableprocess. The multiple parts of each image can then be processed in anysuitable way (e.g., separately, together, etc.) for any remainderprocesses of the method. In a specific example, for instance, each of aset of brain images is divided into left and right hemispheres (e.g.,separated along the longitudinal fissure, separated along a y-axis,separated along a y-axis after the image has been straightened out,etc.), wherein the set of left hemispheres and the set of righthemispheres are processed separately and the results compared.

S210 can optionally include windowing (equivalently referred to hereinas clipping) the image data, which can function to increase the imagecontrast of a predetermined (e.g., desired) range (window) ofpixel/voxel values (e.g., Hounsfield units), eliminate irrelevantinformation (e.g., information not corresponding to regions ofinterest/potential user conditions) from further processing, and/orperform any other suitable function. The threshold values (e.g., HUvalues) determining the window range can optionally be determined basedon any or all of: the type of scan (e.g., contrast, non-contrast, CT,NCCT, MRI, etc.), the body region scanned (e.g., head), suspectedcondition (e.g., ischemic core, ischemic penumbra, ischemic stroke,etc.), patient demographics and/or metadata (e.g., age, gender, etc.),and/or any other suitable information. Additionally or alternatively,the threshold values can be constant for all images.

In some variations (e.g., for detecting brain conditions), the range ofHU values that are retained for processing ranges from a Hounsfield unitjust below that corresponding to soft matter to a Hounsfield unit justabove that corresponding to bone. Additionally or alternatively, awindow can be shifted with respect to this (e.g., anything below bone),narrowed with respect to this (e.g., only encompassing brain tissue),broadened with respect to this, or otherwise selected.

In specific examples, the information retained has HU values between1000 and 2000.

Windowing the data can be performed after one or more registrationprocesses, prior to one or more registration processes, during one ormore registration processes, at another time, or at any combination oftimes. The window preferably ranges from a Hounsfield unit just belowthat corresponding to soft matter to a Hounsfield unit just above thatcorresponding to bone. Additionally or alternatively, a window can beshifted with respect to this (e.g., anything below bone), narrowed withrespect to this (e.g., only encompassing brain tissue), broadened withrespect to this, or otherwise selected.

S210 can further include an effective and/or actual removal (e.g.,assignment of a pixel value of zero, actual removal through cropping,etc.) of one or more regions of the image, which can be performed afterand/or based on a windowing process (e.g., as described above), but canadditionally or alternatively be performed prior to a windowing process,in absence of a windowing process, in a later process (e.g., after asegmentation process), or otherwise performed. In one variation, a setof “white” regions (e.g., regions having a pixel/voxel value of 255,regions having a pixel/voxel value above a predetermined threshold,etc.) are assigned a pixel/voxel value of zero, which—in combinationwith a windowing process as described above—can function to result inthe extraction of only soft matter (e.g., brain matter, blood, cerebralfluid, etc.); this can equivalently be referred to herein as skullstripping. Additionally or alternatively, any other regions can beeffectively and/or actually removed in any suitable way (e.g., throughphotomasks, dilation, erosion, etc.).

S210 can also optionally include the normalization of any or all of theimage data, which can function to enable the comparison of multipleimages with each other, the comparison of one series with a differentseries (e.g., the series of a previously-taken brain scan, the series ofone patient with a reference series, the series of a first patient thatthat of a second patient, etc.), or perform any other suitable function,such as faster training convergence of deep learning models inprocessing. In one variation, the set of voxels in the image data arenormalized such that the voxels have a predetermined mean Hounsfieldunit value (e.g., 24.5 HU, less than 24.5 HU, greater than 24.5 HU,etc.) and a predetermined standard deviation Hounsfield unit value(e.g., 39.5 HU, less than 39.5 HU, greater than 39.5 HU, etc.). Inspecific examples, the image data is normalized by dividing by a fixednumber. Additionally or alternatively, the image data can be normalizedin any other suitable way with respect to any suitable parameter and/orthe method can be performed in absence of a normalization process.

S210 can optionally include organizing the set of images (e.g.,instances, slices, scans, etc.), preferably into a series, butadditionally or alternatively into a study, or any other suitablegrouping of images. The organization is preferably performed in responseto generating a set of images (e.g., at an imaging modality), but canadditionally or alternatively be performed in response to receiving aset of instances at a location (e.g., router, remote computing system,server such as a PACS server, etc.), at the request of an individual(e.g., healthcare worker), in response to a trigger, in response to anyother pre-processing step, or at any other suitable time.

In some variations, the method includes excluding a data packet (e.g.,set of instances) from further processing if one or more of a set ofmetadata are not satisfied, such as, but not limited to, the metadatalisted above.

In a specific example, a bone mask is determined and defined as a set ofvoxels having an HU value above a predetermined threshold (e.g., 750 HU,700 HU, 800 HU, between 600 HU and 900 HU, etc.). The bone mask is thendilated with a series of kernels of increasing size until it completelyencloses a set of voxels of low HU values (e.g., less than thepredetermined threshold), thereby defining a soft matter mask. The softmatter mask is dilated to compensate for the dilation of the bone mask.If the process of defining the soft matter mask fails, this can indicatethat the skull has undergone a craniotomy, which in some cases can beused in determining a diagnosis, informing a contact or second point ofcare, or in any other point in the method. Once the soft matter mask isdilated, the mask can then be applied to the set of instances (e.g.,organized set of instances, series, etc.), and the HU value of voxelsoutside of the mask is set to zero.

In a first set of variations, S210 includes producing a mirror image ofeach of the set of images and overlaying and/or registering each of theset of images with its mirror image. Additionally or alternatively, S210can include resizing each of a set of NCCT images to a predetermineduniform size (e.g., 256×256); windowing the NCCT images (e.g., to removebone matter); and/or any other suitable processes.

4.4 Method—Checking for a Suspected Condition Associated with the DataPacket S220

The method 200 includes checking for a suspected condition andoptionally one or more parameters of the suspected condition (e.g., asshown in FIG. 13 ) associated with the data packet S220, which functionsto determine a region of image data proposed to be affected with apatient condition (e.g., brain ischemia) and inform the remainingprocesses of the method. Additionally or alternatively, S220 canfunction to reduce the time to transfer a patient to a second point ofcare, halt progression of the condition, or perform any other suitablefunction. S220 is preferably fully performed at a remote computingsystem (e.g., remote server, cloud-based server, etc.), furtherpreferably a remote computing system having a graphics processing unit(GPU), but can additionally or alternatively be partially performed atany suitable remote computing system, be partially or fully performed ata local computing system (e.g., workstation), server (e.g., PACSserver), at a processor of a user device, or at any other system. S220is preferably partially or fully performed using software including oneor more algorithms, further preferably one or more multi-step algorithmscontaining steps that are either trained (e.g., trained through machinelearning, trained through deep learning, continuously trained, etc.) ornon-trained (e.g., rule-based image processing algorithms orheuristics). Additionally or alternatively, any software can beimplemented.

S220 preferably includes identifying (e.g., locating, isolating,measuring, quantifying, etc.) and optionally segmenting an affectedbrain region (e.g., injured tissue, ischemic tissue, ischemic coretissue, ischemic penumbra tissue, etc.) within one or more of the set ofimages, thereby indicating a brain condition (e.g., ischemia, ischemicstroke, etc.) or at least a potential brain condition (e.g., ischemia,ischemic core, ischemic stroke). This can be performed through anynumber of computer vision techniques, such as object recognition, objectidentification, object detection, or any other form of image analysis.

In cases of brain ischemia (e.g., ischemic core), the affected brainregion can include and/or be characterized by any or all of:hypodensity, which can be depicted as a dark region in a brain scan(e.g., region having a pixel value [e.g., HU value] above apredetermined threshold, region having a pixel value exceeding aneighboring pixel value by a predetermined threshold, etc.); abnormaltexture, which can be depicted as a loss of delineation in a region ofthe brain scan (e.g., region having an overall contrast [e.g., range ofHU values] below a predetermined threshold, region having a spacingbetween detected features above a predetermined threshold, region havingan expected feature [e.g., gyrus] missing, etc.); sulcal effacement,which can be depicted as a decreased fluid volume of one or more sulci(e.g., region having a distance between adjacent gyri below apredetermined threshold); or any other suitable feature.

As such, identifying an affected brain region (e.g., hypodense brainregion) can include identifying (e.g., indirectly with a machinelearning model, with a classically programmed model, etc.) imagefeatures associated with an ischemic condition based on a trainedmachine learning model. These features can include a pixel value and/orHounsfield unit value outside of a “normal” range (e.g., above apredetermined threshold value, below a predetermined threshold value,outside a range of predetermined values, etc.). This can include, forinstance, any or all of: identifying an image region having a pixelvalue above a predetermined threshold, identifying a region having aHounsfield unit value above a predetermined threshold, identifying aregion having a pixel value below a predetermined threshold, identifyinga region having a Hounsfield unit value below a predetermined threshold,identifying a region which differs in pixel value from adjacent regionsby a value above a predetermined threshold, identifying a region whichdiffers in Hounsfield unit value from adjacent regions by a value abovea predetermined threshold, or any other suitable region having definedby any suitable features. Additionally or alternatively, identifying anaffected brain region can include identifying a region (e.g., of auniform size, of a size above a predetermined threshold, etc.) having adelineation parameter below a predetermined threshold (e.g., bydetecting a change in delineation with respect to an adjacent regionabove a predetermined threshold, by detecting a set of lines in thebrain scan having a length below a predetermined threshold, etc.).Further additionally or alternatively, identifying an affected brainregion can include identifying a region in which a set of sulci haveterminated (e.g., uniformly terminated, suddenly terminated, graduallyterminated, etc.) and/or experienced an abnormal spacing (e.g., throughdetecting a spacing between adjacent sulci below a predeterminedthreshold, through detecting a spacing between adjacent gyri below apredetermined threshold, through detecting a spacing between adjacentsulci above a predetermined threshold, through detecting a spacingbetween adjacent gyri above a predetermined threshold, etc.).Additionally or alternatively, identifying an affected brain region caninclude any other suitable processing of the set of images.

The image region can optionally be compared with one or more thresholds,the one or more thresholds can be any or all of: predetermined (e.g.,constant, based on one or more anatomical values, based on one or morephysiological values, based on an algorithm, based on an average valuefrom a dataset, based on a maximum value from a dataset, based on aminimum value from a dataset, etc.), dynamically determined (e.g.,specific to the user, based on a value in a corresponding region in theopposing hemisphere, based on a value found in the image dataset, etc.),any combination of predetermined and dynamically determined, orotherwise determined (e.g., without a set of thresholds).

Determining that a proposed brain region is potentially affected (e.g.,and suitable for future processing) can require any or all of: thesatisfaction of one of the above criteria (e.g., exceeding a threshold),the satisfaction of a predetermined number (e.g., 2 or more, 3 or more,all, etc.) of the above criteria, the presence of any other suitablecriteria, or be otherwise determined.

Identifying an affected brain region (e.g., region of tissue death)preferably includes image segmentation (e.g., prior to comparing anaffected region with a threshold), wherein the segmentation can includeany or all of: thresholding, clustering methods, dual clusteringmethods, compression-based methods, histogram-based methods,region-growing methods, partial differential equation-based methods,variational methods, graph partitioning methods, watershedtransformations, model based segmentation, multi-scale segmentation,semi-automatic segmentation, trainable segmentation, or any suitableform of segmentation. The method can additionally or alternativelyinclude any number of segmentation post-processing steps, such asthresholding, connectivity analyses, or any other processing. Thesegmentation is preferably performed with a deep learning moduleincluding a convolutional neural network (CNN), further preferably anyor all of: a U-Net, V-net, and/or any suitable feed-forward deep CNN(e.g., using three-dimensional convolutions, two-dimensionalconvolutions, etc.), but can additionally or alternatively be performedusing any suitable models, algorithms, and/or or processes.

In some variations, for instance, S220 includes a segmentation processin the event of a suspected ischemia, wherein the segmentation processsegments regions associated with early ischemic changes through a deepCNN, wherein the deep CNN was trained based on hand annotated trainingdata. The segmentation process is preferably performed on a set of oneor more images (e.g., slices, resized slices, etc.) wherein mirrorimages (e.g., overlaid registered images) are received at the deep CNNas an input, but can additionally or alternatively be performed on theoverlaid image (e.g., registered overlaid image), and/or any othersuitable image(s). In a first specific example, the segmentation processis performed on the set of image slices, wherein the segmentationprocess produces a segmented region corresponding to a suspectedischemic region based on analysis of the set of images and an overlaidset of images with the set of images and its mirror images (e.g., basedon a ratio of voxels in the segmented region relative a mirror imageexceeding a threshold).

The segmentation process can optionally include assigning one or morescores, such as a probability score (e.g., between 0 and 1) to each baseunit (e.g., pixel, voxel, etc.) of the input, which indicates alikelihood that the base unit represents a part of the brain condition(e.g., ischemic core) or set of brain conditions being tested for. Asegmentation region (e.g., initial segmentation region, finalsegmentation region, etc.) is then formed from the base units having aprobability score above a predetermined threshold.

In one variation, the segmentation of a set of NCCT scan slicesfunctions to segment regions which are consistent with an ischemicconditions. In a specific example, a 3D array is formed from the set ofscan slices, the 3D array containing a set of probability values between0 and 1 for each of the set of voxels in a 3D representation of the scanslices, the probability values corresponding to a likelihood that eachvoxel corresponds to an ischemic feature (e.g., as described above),such as based on comparison with a mirror image region. A set of voxels(e.g., contiguous voxels, connected voxels, bordering voxels, etc.),preferably adjacent (e.g., contiguous) voxels (but alternativelynon-adjacent or partially adjacent voxels), having a ratio above apredetermined threshold in comparison with their mirror imagecounterparts represents a region suspected of having an ischemiccondition. The probabilistic output of the network can then be convertedinto a binary mask defined as all voxels having a probability greaterthan this threshold (e.g., 0.5, 0.7, between 0.4 and 0.8, etc.), whichforms the segmentation.

The probability threshold can optionally be dependent on any number offeatures of the segmentation and/or images, such as any or all of: thelocation of the segmented region (e.g., intraparenchymal,intraventricular, epidural, subdural, subarachnoid, etc.), the suspectedcondition (e.g., and a severity of the condition being tested for),metadata, and/or any other suitable features. Alternatively, theprobability threshold can be constant.

Additionally or alternatively, probabilities from multiple voxels can beaggregated (e.g., averaged) and compared with a threshold, a minimumnumber of high probabilities can need to be reached, and/or anyprobabilities can be examined in any suitable ways.

Further additionally or alternatively, the method can be performed inabsence of determining a probability score

S220 can optionally include evaluating one or more exclusion criteria inthe image data(e.g., potential affected brain region, segmented brainregion, etc.), which can function to verify that the image data isrelevant for evaluation in the rest of the method, save time and/orresources by eliminating irrelevant scans, check for a particular set offalse positives (e.g., artifacts, eliminate one or more categories offalse positives while still maintaining an overall high percentage offalse negatives, minimizing a number of false positives, eliminating oneor more categories of false positives while still maintaining an overallhigh percentage of false positives, etc.), route a set of instancescorresponding to one or more exclusion criteria to another workflow in ahealthcare facility, or perform any other suitable function. In somevariations, for instance, a set of exclusion criteria are evaluated,which function to keep “close call” false positives (e.g., questionablepathologies, affected brain tissue, etc.) while eliminating falsepositives caused by non-physiological events (e.g., metal artifact, poorimage quality, etc.). Additionally or alternatively, exclusion criteriaare evaluated to minimize an overall number of false positives, therebyminimizing, for instance, unnecessary interruptions to specialistsand/or any other number of recipients (e.g., clinical trial principalinvestigators). Alternatively, the method can partially or fully processall sets of instances.

Evaluating exclusion criteria preferably includes checking for any orall of: the presence of an artifact in one or more of the set ofinstances (e.g., metallic artifact, aneurysm clip, etc.), impropertiming at which the set of instances were taken at an imaging modality(e.g., premature timing, improper timing of a bolus, etc.), one or moreincomplete regions (e.g., features, anatomical features, etc.) in theset of instances (e.g., incomplete skull, incomplete vessel, incompletesoft matter region, etc.), an incorrect scan type or body part (e.g.,non-head CT scan, non-contrast CT scan, etc.), poor image quality (e.g.,blurry images, low contrast, etc.), movement of the patient during thescan (e.g., manifesting as bright streaks in one or more images), acalcification, or any other suitable exclusion criteria.

In one variation, a set of images (e.g., instances, series, etc.) areevaluated to determine if an artifact is present, wherein the set ofimages is excluded from further processing if an artifact is found. In aspecific example, the method includes inspecting the HU values of voxelsin a soft matter mask, wherein voxels having a value above apredetermined threshold (e.g., 3000 HU, between 2000 and 4000 HU, etc.)are determined to be a metallic artifact (e.g., aneurysm clip).

Checking for exclusion criteria can optionally additionally oralternatively include comparing a size of a region with a size criteria,wherein connected components (e.g., segmentations) are evaluated basedon any or all of: area (e.g., number of pixels), volume (e.g., volume inmL, number of voxels etc.), one or more characteristic dimensions (e.g.,maximum dimension, minimum dimension, length, width, maximum length,maximum width, thickness, radius, diameter, etc.), or any other suitablesize category. The size can be compared with a threshold, wherein if thesize is below the threshold (e.g., indicating an artifact, indicatingnoise, etc.), the component is eliminated from further processing and/orconsideration. Additionally or alternatively, if the size is above athreshold (e.g., indicating an image quality issue, indicating anischemic core condition too severe for intervention, indicating thepatient moving during the scan, etc.), the component can be eliminatedfrom further processing and/or consideration; if the size is within arange of thresholds, the component can be eliminated from furtherprocessing and/or consideration; if the size is outside a range ofthresholds, the component can be eliminated from further processingand/or consideration; or the component can be otherwise evaluated and/orfurther processed (e.g., evaluated to determine an ischemic core vs. anischemic penumbra).

In some variations, for instance, a size of the segmented region iscompared with a minimum volume threshold, wherein if the segmentedregion size is below the minimum volume threshold, the segmented regionis eliminated from further consideration of the condition, and whereinif the segmented region size is above the minimum volume threshold, thesegmented region continues to further processing and/or results in thedetermination of the suspected condition. Additionally or alternatively,the size threshold can include a 2D size threshold (e.g., per slice), 1Dsize threshold (e.g., largest length of segmentation in a slice, largestwidth of segmentation in a slice, thickness, etc.), and/or any othersuitable thresholds.

Checking for exclusion criteria can optionally further additionally oralternatively include comparing a location of the region with a locationcriteria, wherein connected components (e.g., segmentations) areevaluated based on their proximity to another component, such as anotheranatomical component (e.g., bone, skull, etc.), a particular brainregion or brain feature (e.g., particular sulcus, lobe, covering, etc.),an image feature (e.g., an image edge, etc.), or any other component orgeneral location. In the event that the relative location (e.g.,distance, proximity, etc.) is below a threshold (indicating that thecomponent is too close), the component can be discarded.

The condition typically refers to a hypothesized patient condition ordiagnosis (e.g., ischemic core, ICH, LVO, aneurysm, stroke, etc.) butcan additionally or alternatively include a severity (e.g., based on apredetermined severity scale), an urgency, or any other characteristic.

S220 preferably produces as an output a determination of a patientcondition, wherein this determination can trigger one or more outcomes(e.g., as described below), prompt further analysis (e.g., thedetermination of one or more associated parameters), be recorded (e.g.,in a patient record), and/or be otherwise used. In variations ofischemic core, for instance, further analysis can be triggered todetermine any or all of: an amount of affected (e.g., compromised)brain, an amount of unaffected brain, the particular affected region(e.g., brain territory, brain lobe, etc.), the function of the affectedregion (e.g., motor control, memory, emotion, etc.), a trajectory of theaffected region (e.g., rate of spreading, blood flow rate, etc.), or anyother suitable parameter.

In one variation, S220 includes: receiving image data including a brainimage and its mirror image (e.g., individually, in an overlaid fashion,individually after being registered in an overlaid fashion, etc.);segmenting a region of the image data (e.g., in the brain image, in anoverlaid image, etc.) indicating a potential ischemia (e.g., earlyischemia) with a CNN (e.g., V-net, U-net, etc.); and determining thesuspected condition based on the segmentation (e.g., based on a size ofthe region in comparison with one or more volume thresholds, based on anaggregated probability score, based on a binary presence of asegmentation, etc.). Additionally or alternatively, S220 can includecomparing one or more parameters (e.g., HU value, pixel value, number ofpixels, length, etc.) of the segmented region with its overlappingregion in the mirror image (e.g., corresponding region in the opposinghemisphere); and determining a potential ischemia based on thiscomparison (e.g., exceeds the corresponding parameter by a predeterminedthreshold, the corresponding parameter exceeds its value by apredetermined threshold, etc.). In a specific example, for instance,S220 includes identifying a relatively dark region in a left hemisphereof an image; identifying the corresponding region in the righthemisphere based on the registered overlaid image; determining adarkness associated with this corresponding region; comparing the twodarknesses and determining that the left hemisphere region's darknessexceeds the corresponding right hemisphere region's darkness by apredetermined threshold; and determining that the patient is potentiallyexperiencing an ischemic core condition.

4.5 Method—In an Event that the Suspected Condition is Detected,Determining a Recipient Based on the Suspected Condition S230

The method includes in an event that the suspected condition isdetected, determining a recipient based on the suspected condition S230,which functions to facilitate the treatment (e.g., triage, acceptanceinto a clinical trial, etc.) of the patient.

S230 can additionally, alternatively, and/or equivalently includedetermining a treatment option S230, preferably in the event that acondition is detected (e.g., based on a comparison with a threshold,based on a binary presence, etc.) but can additionally or alternativelydetermine a treatment option when a condition is not detected, when ananalysis is inconclusive, or in any suitable scenario. S230 can functionto match the patient with a specialist, initiate the transfer of apatient to a 2^(nd) point of care (e.g., specialist facility), initiatethe transfer of a specialist to a 1^(st) point of care, or initiatetreatment of a patient (e.g., surgery, stent placement, mechanicalthrombectomy, etc.) within the 1^(st) point of care, initiate thematching of a patient to a clinical trial, or perform any other suitablefunction. In some variations, the treatment option is a 2^(nd) point ofcare, wherein it is determined (e.g., suggested, assigned, etc.) thatthe patient should be treated at the 2^(nd) point of care. Additionallyor alternatively, the treatment option can be a procedure (e.g.,surgical procedure, surgical clipping, mechanical thrombectomy,placement of an aneurysm coil, placement of a stent, retrieval of athrombus, stereotactic radiosurgery, etc.), treatment (e.g., tissueplasminogen activator (TPA), pain killer, blood thinner, etc.), recoveryplan (e.g., physical therapy, speech therapy, etc.), or any othersuitable treatment.

The recipient and/or treatment is preferably determined based on aparameter determined from the data packet (e.g., binary presence of acondition, comparison of a parameter with a threshold, etc.), but canadditionally or alternatively be determined based on additional data,such as patient information (e.g., demographic information, patienthistory, patient treatment preferences, etc.), input from one or moreindividuals (e.g., power of attorney, attending physician, emergencyphysician, etc.), a consensus reached by multiple recipients of anotification (e.g., majority of members of a care team, all members of acare team, etc.), or any other suitable information.

S230 is preferably at least partially performed with software operatingat the remote computing system (e.g., remote server) but canadditionally or alternatively be performed at a remote computing systemseparate from a previous remote computing system, a local computingsystem (e.g., local server, virtual machine coupled to healthcarefacility server, computing system connected to a PACS server), or at anyother location.

S230 is preferably performed after a patient condition has beendetermined during the method 200. Additionally or alternatively, S230can be performed after a patient condition has been determined in analternative workflow (e.g., at the 1^(st) point of care, at aradiologist workstation during a standard radiology workflow, in thecase of a false negative, etc.), prior to or absent the determination ofa patient condition (e.g., based on an input from a healthcare worker atthe remote computing system, when patient is admitted to 1^(st) point ofcare, etc.), multiple times throughout the method (e.g., after a firsttreatment option fails, after a first specialist is unresponsive, suchas after a threshold amount of time, such as 30 seconds, 1 minute, 2minutes, etc.), or at any other time during the method.

S230 preferably determines a recipient (and/or a treatment option) witha lookup table located in a database accessible at remote computingsystem (e.g., cloud-computing system). Additionally or alternatively, alookup table can be stored at a healthcare facility computing system(e.g., PACS server), in storage at a user device, or at any otherlocation.

In other variations, the recipient and/or treatment option can bedetermined based on one or more algorithms (e.g., predictive algorithm,trained algorithm, etc.), one or more individuals (e.g., specialist,care team, clinical trial coordinator, etc.), a decision support tool, adecision tree, a set of mappings, a model (e.g., deep learning model),or through any other process or tool.

The lookup table preferably correlates a 2^(nd) point-of-care (e.g.,healthcare facility, hub, physician, specialist, neuro-interventionist,etc.), further preferably a specialist or contact (e.g., administrativeworker, emergency room physician, etc.) associated with the 2^(nd) pointof care, with a patient condition (e.g., presence of an LVO, presence ofa pathology, severity, etc.), but can additionally or alternativelycorrelate a treatment option with the patient condition, and/or anyother suitable recipient (e.g., at a 1^(st) point of care, associatedwith a clinical trial, etc.) with the condition. The lookup table canfurther additionally or alternatively correlate a treatment option withsupplementary information (e.g., patient history, demographicinformation, heuristic information, etc.).

The recipient, equivalently referred to herein as contact, (e.g.,healthcare provider, neuro-interventional specialist, principalinvestigator, stroke care team member, clinical trial enrollmentcommittee, etc.) is preferably a healthcare worker, but can additionallyor alternatively be any individual associated with the treatment of thepatient and/or be associated with any healthcare facility (e.g., priorhealthcare facility of patient, current healthcare facility, recommendedhealthcare facility) related to the patient. The contact is furtherpreferably a specialist (e.g., neuro-interventional specialist,neurosurgeon, neurovascular surgeon, general surgeon, cardiacspecialist, etc.) but can additionally or alternatively include anadministrative worker associated with a specialist, multiple points ofcontact (e.g., ranked order, group, etc.), or any other suitableindividual or group of individuals. The contact is preferably associatedwith a hub facility, wherein the hub facility is determined as an optionfor second point of care, but can additionally or alternatively beassociated with a spoke facility (e.g., current facility, futurefacility option, etc.), an individual with a relation to the patient(e.g., family member, employer, friend, acquaintance, emergency contact,etc.), or any other suitable individual or entity (e.g., employer,insurance company, etc.). Additionally or alternatively, the contact canbe an individual associated with a clinical trial (e.g., principalinvestigator at a 1^(st) point of care, principal investigator at a2^(nd) point of care, approval/enrollment committee to approve a patientfor a clinical trial, etc.), and/or any other suitable individual.

The lookup table is preferably determined based on multiple types ofinformation, such as, but not limited to: location information (e.g.,location of a 1^(st) point of care, location of a 2^(nd) point of care,distance between points of care, etc.), temporal information (e.g., timeof transit between points of care, time passed since patient presentedat 1^(st) point of care, etc.), features of condition (e.g., size ofocclusion, severity of condition, etc.), patient demographics (e.g.,age, general health, history, etc.), specialist information (e.g.,schedule, on-call times, historic response time, skill level, years ofexperience, specialty procedures, historic success or procedures, etc.),healthcare facility information (e.g., current number of patients,available beds, available machines, etc.), but can additionally oralternatively be determined based on a single type of information or inany other suitable way. Information can be actual, estimated, predicted,or otherwise determined or collected.

In some variations, the method 200 includes transmitting information(e.g., patient condition, data determined from analysis, optimal set ofinstances, series, data packet, etc.) to the computing system associatedwith the lookup table.

4.6 Method—Preparing a Data Packet for Transfer S240

The method 200 can include preparing a data packet for transfer, whichcan function to produce a compressed data packet, partially or fullyanonymize a data packet (e.g., to comply with patient privacyguidelines, to comply with Health Insurance Portability andAccountability Act (HIPAA) regulations, to comply with General DataProtection Regulation (GDRP) protocols, etc.), minimize the time totransfer a data packet, annotate one or more images, or perform anyother suitable function. Additionally or alternatively, any or all of adata packet previously described can be transferred.

The data packet is preferably transferred (e.g., once when data packetis generated, after a predetermined delay, etc.) to a contact, furtherpreferably a specialist (e.g., associated with a 2^(nd) point of care,located at the 1^(st) point of care, etc.), but can additionally oralternatively be sent to another healthcare facility worker (e.g., at1^(st) point of care, radiologist, etc.), an individual (e.g., relative,patient, etc.), a healthcare facility computing system (e.g.,workstation), a server or database (e.g., PACS server), or to any othersuitable location.

S240 preferably includes compressing a set of images (e.g., series), butcan additionally or alternatively leave the set of images uncompressed,compress a partial set of images (e.g., a subset depicting thecondition), or compress any other part of a data packet. Compressing thedata packet functions to enable the data packet to be sent to, receivedat, and viewed on a user device, such as a mobile device. Compressingthe data packet can include any or all of: removing a particular imageregion (e.g., region corresponding to air, region corresponding to hardmatter, region without contrast dye, irrelevant anatomical region,etc.), thresholding of voxel values (e.g., all values below apredetermined threshold are set to a fixed value, all values above apredetermined threshold are set to a fixed value, all values below −500HU are set to −500, all voxel values corresponding to a particularregion are set to a fixed value, all voxels corresponding to air are setto a predetermined fixed value, etc.), reducing a size of each image(e.g., scale image size by factor of 0.9, scale image size by factor of0.7, scale image size by factor of 0.5, scale image size by a factorbetween 0.1 and 0.9, reduce image size by a factor of 4, etc.), orthrough any other compression method.

In one variation, the reduction in size of a set of images can bedetermined based on one or more memory constraints of the receivingdevice (e.g., user device, mobile device, etc.).

In some variations, such as those involving a patient presenting with abrain condition (e.g., ischemic core), the images taken at an imagingmodality (e.g., CT scanner) are compressed by determining an approximateor exact region in each image corresponding to air (e.g., based on HUvalue, based on location, based on volume, etc.) and setting the airregion (e.g., voxels corresponding to the air region, pixelscorresponding to the air region, etc.) to have a fixed value.Additionally or alternatively, any non-critical region (e.g., bone,unaffected region, etc.) or other region can be altered (e.g., set to afixed value, removed, etc.) during the compression. In a specificexample, for instance, a set of voxels corresponding to air are set toall have a common fixed value (e.g., an upper limit value, a lower limitvalue, a value between 0 and 1, a predetermined value, etc.).

In some variations, S240 includes identifying an optimal visualizationto be transmitted (e.g., from a remote computing system) and received(e.g., at a user device), which functions to prepare an optimal outputfor a 2^(nd) point of care (e.g., specialist), reduce the time requiredto review the data packet, bring attention to the most relevant imagedata, or to effect any other suitable outcome.

In some variations, this involves a reverse registration process. In aspecific example, for instance, this is done through maximum intensityprojection (MIP), where an optimal range of instances is determinedbased on which images contain the largest percentage of the segmentedanatomical region of interest in a MIP image.

Additionally or alternatively, S240 can include removing and/or altering(e.g., encrypting) metadata or any unnecessary, private, confidential,or sensitive information from the data packet. In some variations,patient information (e.g., patient-identifiable information) is removedfrom the data packet in order to comply with regulatory guidelines. Inother variations, all metadata are extracted and removed from the datapacket.

S240 can optionally include annotating one or more images in the datapacket, which can function to draw attention to one or more features ofthe images, help a specialist or other recipient easily and efficientlyassess the images, and/or perform any other suitable functions.

Annotating the images can optionally include adding (e.g., assigning,overlaying, etc.) one or more visual indicators (e.g., labels, text,arrows, highlighted or colored regions, measurements, etc.) to one ormore images. The incorporation of the visual indicators can bedetermined based on any or all of: the suspected condition (e.g., typeof visual indicators designated for the condition based on a lookuptable), one or more thresholds (e.g., size thresholds), features of thesuspected condition/pathology (e.g., ischemic penumbra vs. ischemiccore, location of condition within brain, etc.), preferences (e.g.,specialist preferences, point of care preferences, etc.), guidelines(e.g., patient privacy guidelines), the results of one or more deeplearning models, and/or any other suitable factors or information. Invariations with visual indicators, a table/key can optionally beprovided to explain the visual indicators, which can include any or allof: a color key defining what colors correspond to; one or moreparameters associated with an indicated region or feature (e.g., volumeof core); one or more parameters associated with the image(s) as a whole(e.g., total volume measured across all regions, number of regionsindicated, etc.); and/or any other suitable information.

Images can additionally or alternatively be annotated with one or moremetrics, such as one or more parameters (e.g., size as described above);scores (e.g., a clinical score, a severity score, etc.); instructions(e.g., recommended intervention); and/or any other suitable information.

Additionally or alternatively to being annotated on an image, any or allof the annotations can be provided in a separate notification, such as amessage, document, and/or provided in any other suitable way.

The annotations are preferably determined automatically (e.g., at aremote computing system implementing the deep learning models, at aclient application, at a mobile device executing a client application,etc.), but can additionally or alternatively be determined manually,verified manually, or otherwise determined.

In some variations, for instance, a size (e.g., volume) of the affectedarea (e.g., ischemia, ischemic core, etc.) has been found to be helpfulin decision-making for the specialist (e.g., determining whether or notto intervene, determining a type of intervention, etc.) and is indicatedto the specialist on one or more images transmitted to him or her. Inspecific examples, the region is indicated by (e.g., highlighted in,outlined in, etc.) one of a set of colors, wherein the color canindicate any or all of: a type of ischemia, a calculated size (e.g.,volume) of the affected region, and/or any other suitable information.

S240 can optionally include prescribing a subset of images to be viewedby the recipient and/or an order in which images should be viewed (e.g.,the particular image shown first to the recipient upon opening a clientapplication in response to receiving a notification, the image shown inthe thumbnail of a notification, the only image or subset of imagessent, etc.). This can include, for instance, selecting the image orimages indicating the suspected condition (e.g., all slices containingthe suspected condition, a single slice containing the suspectedcondition, the slice containing the largest cross section of a suspectedcondition, the slice containing an important or critical feature of thesuspected condition, etc.) for viewing by the recipient. In specificexamples, the recipient (e.g., specialist) is sent a notificationwherein when the recipient opens the notification on a device (e.g.,mobile device), the image corresponding to the suspected condition isshown first (and optionally corresponds to a thumbnail image shown tothe recipient in the notification).

The notification(s) and/or image(s) provided to a recipient arepreferably provided within a threshold time period from the time inwhich the patient is imaged (e.g., 15 minutes, between 10 and 15minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes, 5minutes, 3 minutes, 2 minutes, 1 minute, etc.), but can additionally oralternatively be provided in another suitable time frame (e.g., greaterthan 15 minutes), prior to a next action in the standard of care (e.g.,prior to a decision is made by a radiologist in a parallel workflow),and/or at any other time(s).

In some variations, S240 includes storing a dataset (e.g., at a remoteserver, at a local server, at a PACS server, etc.). In one example,metadata are extracted from the image data and stored separately fromimage data in a relational database. In another example, any or all ofthe data packet are stored (e.g., temporarily, permanently, etc.) to beused in one or more future analytics processes, which can function toimprove the method, better match patients with suitable treatmentoptions, or for any other suitable purpose.

S240 can optionally include applying a low bandwidth implementationprocess, which can function to reduce the time until a specialistreceives a first piece of data or data packet (e.g., an incompleteseries, incomplete study, single instance, single image, optimal image,image showing occlusion, etc.), reduce the processing required to informa specialist of a potential patient condition, reduce the amount of datarequired to be reviewed by a specialist, reduce the amount of data beingtransmitted from a remote computing system to a mobile device, orperform any other suitable function. The low bandwidth implementationprocess can include any or all of: organizing (e.g., chunking) data(e.g., chunking a series of images based on anatomical region),reordering data (e.g., reordering slices in a CT series), transmitting aportion (e.g., single image, single slice, etc.) of a data packet (e.g.,series, study, set of images, etc.) to a device (e.g., user device,mobile device, healthcare facility workstation, computer, etc.), sendingthe rest of the data packet (e.g., only in response to a request, aftera predetermined time has passed, once the data packet has been fullyprocessed, etc.), or any other process. In a specific example, forinstance, the image data (e.g., slices) received at a remote computingsystem from a scanner are chunked, reordered, and a subset of slices(e.g., single slice) is sent to the device associated with a specialistfirst (e.g., prior to sending a remaining set of slices, in absence ofsending a remainder of slices, etc.).

In some variations, S240 includes implementing a buffering protocol,which enables a recipient (e.g., specialist) to start viewing images(e.g., on a mobile device) prior to all of the images being loaded atthe device (e.g., user device, mobile device, etc.) at which therecipient is viewing images. Additionally or alternatively, thebuffering protocol can include transmitting images to the recipient inbatches, annotating images in batches, or otherwise implementing abuffering protocol.

Additionally or alternatively, S240 can include any other suitable stepsperformed in any suitable order.

The method can additionally or alternatively include any other suitablesub-steps for preparing the data packet.

In a first set of variations, S240 includes determining a subset of oneor more images conveying information related to a suspected patientcondition (e.g., showing the presence of the condition, showing thelargest region of the condition, showing a particular feature of thecondition, etc.); optionally annotating the images (e.g., to point outthe condition); and preparing a notification to be sent to thespecialist, wherein the notification instructs the specialist to view atleast the subset of images and optionally includes a thumbnail depictingone of the set of images which the specialist can view prior to viewingthe images.

4.7 Method—Transmitting Information to a Device Associated with aRecipient S250

Transmitting information to a device associated with a recipient (e.g.,at the 1^(st) point of care, at a 2^(nd) point of care, a specialist,etc.) S250 (e.g., as shown in FIG. 6 ) can optionally function toinitiate a pull from a 1^(st) point of care to a 2^(nd) point of care,which can decrease time to care, improve quality of care (e.g., bettermatch between patient condition and specialist), or have any othersuitable outcome. Preferably, the 2^(nd) point of care is a hub facility(e.g., specialist facility, interventional center, comprehensive strokecenter, etc.). In some variations, the 1^(st) point of care (e.g.,healthcare facility at which patient initially presents) also functionsas the 2^(nd) point of care, such as when a suitable specialist isassociated with the 1^(st) point of care, the 1^(st) point of care is ahub (e.g., specialist facility, interventional center, comprehensivestroke center, etc.), it is not advised to transfer the patient (e.g.,condition has high severity), or for any other reason. Additionally oralternatively, S250 can function to enroll a patient in a clinicaltrial, treat the patient (e.g., at the 1^(st) point of care, at a 2^(nd)point of care, etc.), and/or perform any other suitable functions.

S250 is preferably performed after a data packet (e.g., compressed datapacket, encrypted data packet, etc.) and a recipient have beendetermined, but can additionally or alternative be performed at any orall of: in response to a 2^(nd) point of care being determined, multipletimes throughout the method (e.g., to multiple recipients, with multipledata packets, with updated information, after a predetermined amount oftime has passed since a notification has been sent to a first choicespecialist, etc.), or at any other time during the method 200.

The device is preferably a user device, further preferably a mobiledevice. Examples of the user device include a tablet, smartphone, mobilephone, laptop, watch, wearable device (e.g., glasses), or any othersuitable user device. The user device can include power storage (e.g., abattery), processing systems (e.g., CPU, GPU, memory, etc.), useroutputs (e.g., display, speaker, vibration mechanism, etc.), user inputs(e.g., a keyboard, touchscreen, microphone, etc.), a location system(e.g., a GPS system), sensors (e.g., optical sensors, such as lightsensors and cameras, orientation sensors, such as accelerometers,gyroscopes, and altimeters, audio sensors, such as microphones, etc.),data communication system (e.g., a WiFi module, BLE, cellular module,etc.), or any other suitable component.

The device is preferably associated with (e.g., owned by, belonging to,accessible by, etc.) a specialist or other individual associated withthe 2^(nd) point of care, but can additionally or alternatively beassociated with an individual or computing system at the 1^(st) point ofcare, the patient, a clinical trial, or any other suitable individual orsystem.

In one variation, the device is a personal mobile phone of a specialist.In another variation, the device is a workstation at a healthcarefacility (e.g., first point of care, second point of care, etc.).

In some variations, information is sent to multiple members (e.g., allmembers) of a care team or clinical trial team, such as the care teamwho is treating, may treat the patient, or and/or will treat thepatient. This can enable the care team members to do any or all of: makea decision together (e.g., transfer decision, treatment decision, etc.);communicate together (e.g., through the client application); and/orperform any other function.

The information preferably includes a data packet, further preferablythe data packet prepared in S240. Additionally or alternatively, theinformation can include a subset of a data packet, the original datapacket, any other image data set, or any other suitable data. Theinformation further preferably includes a notification, wherein thenotification prompts the individual to review the data packet at thedevice (e.g., a message reciting “urgent: please review!”). Thenotification can optionally include a thumbnail with a selected image(e.g., image indicating patient condition), which a recipient can viewquickly, such as prior to the recipient unlocking device to which thenotification is sent. Additionally or alternatively, the notificationcan prompt the individual to review data (e.g., original data packet,uncompressed images, etc.) at a separate device, such as a workstationin a healthcare facility, a PACS server, or any other location. Furtheradditionally or alternatively, the notification can include any suitableinformation, such as, but not limited to: instructions (e.g., fortreating patient, directions for reaching a healthcare facility),contact information (e.g., for emergency physician at first point ofcare, administrative assistant, etc.), patient information (e.g.,patient history), or any other suitable information.

The notification preferably includes an SMS text message but canadditionally or alternatively include a message through a clientapplication (e.g., as described above, image viewing application,medical imaging application, etc.), an email message (e.g.,de-identified email), audio notification or message (e.g., recordingsent to mobile phone), push notification, phone call, a notificationthrough a medical platform (e.g., PACS, EHR, EMR, healthcare facilitydatabase, etc.), or any other suitable notification.

One or more features of a notification can optionally convey a severityof the patient condition and/or an urgency of receiving a response froma recipient, which can function to adequately alert the recipient andproperly prioritize care of the patient (e.g., relative to otherpatients). In specific examples, for instance, an audio cue associatedwith a notification indicates an urgency of treating a patient, so thata recipient of the message knows to immediately review the images andtriage the patient.

The information is preferably sent to the device through a clientapplication executing on the user device but can additionally oralternatively be sent through a messaging platform, web browser, orother platform. In some variations, the information is sent to alldevices (e.g., mobile phone, smart watch, laptop, tablet, workstation,etc.) associated with the recipient (e.g., specialist), such as alldevices executing the client application associated with the recipient,which functions to increase the immediacy in which the recipient isnotified.

In one variation, S240 and S250 include preparing a notification to besent to a device (e.g., user device, mobile device, etc.) associatedwith a recipient (e.g., a specialist), wherein the notification includesa thumbnail (e.g., as shown in FIG. 10 ) indicating a selected image(e.g., compressed image showing a suspected condition), along with amessage instructing the recipient to review the images in a clientapplication, and optionally the original images at a workstationafterward. In a first set of specific examples, upon detection that aread receipt has not been received (e.g., at the remote computingsystem) in a predetermined amount of time (e.g., 30 seconds, 1 minute, 2minutes, between 0 seconds and 2 minutes, 3 minutes, between 2 minutesand 3 minutes, 5 minutes, greater than 5 minutes, less than 10 minutes,etc.), a second notification is transmitted to a second recipient (e.g.,a second specialist). In a second set of specific examples, sending thenotification further triggers and/or enables communication to beestablished among multiple members of a care team (e.g., a stroke team),such as through a messaging component of the client application, whereinthe images can be viewed and discussed among the care team members. In athird set of specific examples, a notification is sent to specialist ona mobile device of the specialist, compressed images are previewed onthe specialist mobile device, and the specialist is notified as beingresponsible for viewing non-compressed images on a diagnostic viewer andengaging in appropriate patient evaluation and relevant discussion witha treating physician before making care-related decisions or requests.

In a second variation, S240 and S250 include preparing a notification tobe sent to a clinical trial research coordinator, such as a principalinvestigator, wherein the notification indicates that the patient is apotential candidate for a clinical trial (e.g., based on the detectionof a suspected condition, based on a set of clinical trial inclusioncriteria, etc.). In specific examples, a notification can be sent (e.g.,automatically, triggered by the principal investigator, etc.) to amembers of a clinical trial committee (e.g., physician committee),wherein approval is granted by the committee members (e.g., a majority,all, at least one, a predetermined number or percentage, etc.), such asthrough the client application.

A notification can optionally be sent which prompts the individual toprovide an input, wherein the input can indicate that the individualwill view, has viewed, or is in the process of viewing the information(e.g., image data), sees the presence of a condition (e.g., truepositive, serious condition, time-sensitive condition, etc.), does notsee the presence of a condition (e.g., false positive, seriouscondition, time-sensitive condition, etc.), has accepted treatment ofthe patient (e.g., swipes right, swipes up, clicks a check mark, etc.),has denied treatment of the patient (e.g., swipes left, swipes down,clicks an ‘x’, etc.), wants to communicate with another individual(e.g., healthcare worker at 1^(st) point of care), such as through amessaging platform (e.g., native to the device, enabled by the clientapplication, etc.), or any other input. In some variations, one or moreadditional notifications are provided to the individual (e.g., based onthe contents of the input), which can be determined by a lookup table,operator, individual, decision engine, or other tool. In one example,for instance, if the individual indicates that the condition is a truepositive, information related to the transfer of the patient (e.g.,estimated time of arrival, directions to the location of the patient,etc.) can be provided (e.g., in a transfer request, wherein patienttransfer to a specified location, such as the 2^(nd) point of care, canbe initiated upon transfer request receipt). In some variants, the data(e.g., images) are displayed on the user device (e.g., mobile device,workstation) in response to user interaction with the notification(e.g., in response to input receipt). However, the input can trigger anysuitable action or be otherwise used.

Additionally or alternatively, an input can automatically be receivedfrom the client application, such as a read receipt when the individualhas opened the data packet, viewed the notification, or interacted withthe client application in any other suitable way. In one example, if aread receipt is not received (e.g., at the remote computing system) fromthe device within a predetermined amount of time (e.g., 10 seconds), asecond notification and/or data packet (e.g., compressed set of images)are sent to a second individual (e.g., second choice specialist based ona lookup table).

In some variations, various outputs can be sent from the clientapplication (e.g., at the user device) to one or more recipients (e.g.,to a second user device, client application on a work station, on acomputing system, etc.), such as recipients associated with a firstpoint of care (e.g., radiologists, emergency physicians, etc.). Theoutputs can be determined based on the inputs received at the clientapplication associated with the individual (e.g., acceptance of case,verification of true positive, etc.), based on a lookup table, orotherwise determined. The outputs preferably do not alter the standardradiology workflow (e.g., are not shared with radiologists; radiologistsare not notified), which functions to ensure that the method 200 is atrue parallel process, and that the standard radiology workflow resultsin an independent assessment of the patient, but can additionally oralternatively cut short a workflow, bring a specialist in on the patientcase earlier than normal, or affect any other process in a healthcarefacility.

The method can additionally or alternatively include initiating thetransfer of a patient, wherein the transfer includes a recommendationthat the patient be considered for a clinical and/or research trial,based on one or more of: a suspected clinical condition of the patient(e.g., ischemic core), patient information (e.g., demographicinformation), a patient's willingness or potential willingness toparticipate, and/or any other suitable information. Initiating therecommendation can include transmitting any or all of the notificationsdescribed above (e.g., text message, call, email, etc.) to a specialistinvolved in the clinical and/or research trial, a specialist who hasactively turned on notifications for clinical trial recruitment, aresearcher, a research principal investigator, an administrativeassistant, the patient himself, or any other suitable entity orindividual.

4.8 Method—Receiving an Input from the Recipient

The method 200 can include receiving an input from the recipient, whichfunctions to determine a next step for the patient, and can include anyor all of: a confirmation of the suspected condition; a rejection of thesuspected condition (e.g., false positive); an acceptance by aspecialist and/or care team (e.g., stroke team) to treat the patient(e.g., at a 1^(st) point of care, at a 2^(nd) point of care, etc.); arejection of a specialist and/or care team to treat the patient; a readreceipt and/or an indication of a lack or a read receipt within apredetermined time threshold; an approval to enroll the patient in aclinical trial; and/or any other suitable input.

In some variations, a notification is sent in S250 which prompts theindividual to provide an input, wherein the input can indicate that theindividual will view, has viewed, or is in the process of viewing theinformation (e.g., image data), sees the presence of a condition (e.g.,true positive, serious condition, time-sensitive condition, etc.), doesnot see the presence of a condition (e.g., false positive, seriouscondition, time-sensitive condition, etc.), has accepted treatment ofthe patient (e.g., swipes right, swipes up, clicks a check mark, etc.),has denied treatment of the patient (e.g., swipes left, swipes down,clicks an ‘x’, etc.), wants to communicate with another individual(e.g., healthcare worker at 1^(st) point of care), such as through amessaging platform (e.g., native to the device, enabled by the clientapplication, etc.), or any other input. In some variations, one or moreadditional notifications are provided to the individual (e.g., based onthe contents of the input), which can be determined by a lookup table,operator, individual, decision engine, or other tool. In one example,for instance, if the individual indicates that the condition is a truepositive, information related to the transfer of the patient (e.g.,estimated time of arrival, directions to the location of the patient,etc.) can be provided (e.g., in a transfer request, wherein patienttransfer to a specified location, such as the 2^(nd) point of care, canbe initiated upon transfer request receipt). In some variants, the data(e.g., images) are displayed on the user device (e.g., mobile device,workstation) in response to user interaction with the notification(e.g., in response to input receipt). However, the input can trigger anysuitable action or be otherwise used.

Additionally or alternatively, an input can automatically be receivedfrom the client application, such as a read receipt when the individualhas opened the data packet, viewed the notification, or interacted withthe client application in any other suitable way. In one example, if aread receipt is not received (e.g., at the remote computing system) fromthe device within a predetermined amount of time (e.g., 10 seconds), asecond notification and/or data packet (e.g., compressed set of images)are sent to a second individual (e.g., second choice specialist based ona lookup table).

In some variations, various outputs can be sent from the clientapplication (e.g., at the user device) to one or more recipients (e.g.,to a second user device, client application on a work station, on acomputing system, etc.), such as recipients associated with a firstpoint of care (e.g., radiologists, emergency physicians, etc.). Theoutputs can be determined based on the inputs received at the clientapplication associated with the individual (e.g., acceptance of case,verification of true positive, etc.), based on a lookup table, orotherwise determined. The outputs preferably do not alter the standardradiology workflow (e.g., are not shared with radiologists; radiologistsare not notified), which functions to ensure that the method 200 is atrue parallel process, and that the standard radiology workflow resultsin an independent assessment of the patient, but can additionally oralternatively cut short a workflow, bring a specialist in on the patientcase earlier than normal, or affect any other process in a healthcarefacility.

The outputs can include any or all of: the suspected condition,parameters (e.g., volume of an ischemic core) and/or scores (e.g.,severity score, urgency score, etc.) associated with the suspectedcondition; the selection of one or more recipients of a notification(e.g., established and/or proposed care team of the patient); a proposedand/or confirmed intervention for the patient (e.g., type of procedure);an updated status (e.g., location, health status, intervention status,etc.) of one or more patients (e.g., a centralized list of all patientsbeing reviewed by and/or treated by a specialist; a consent of thepatient (e.g., for a clinical trial); an estimated parameter of thepatient (e.g., estimated time of arrival at a second point of care);and/or any other suitable outputs.

4.9 Method—Initiating Treatment of the Patient

The method can additionally or alternatively include initiatingtreatment (e.g., transfer) of the patient, wherein the treatment caninclude any or all of the treatment options described above, such as ayor all of: a point of care (e.g., remain at 1^(st) point of care, betransferred to a 2^(nd) point of care, etc.) at which the patient willbe treated; a procedure to treat the suspected condition; a specialistand/or care team to be assigned to the patient; a clinical trial inwhich to enroll the patient; and/or any other suitable treatments.

In variations involving recommending the patient for a clinical trial,initiating treatment of the patient can include receiving arecommendation that the patient be considered for a clinical and/orresearch trial, based on one or more of: a suspected clinical conditionof the patient (e.g., ischemic core, ischemic penumbra, etc.), patientinformation (e.g., demographic information), a patient's willingness orpotential willingness to participate, and/or any other suitableinformation. Initiating the recommendation can include transmitting anyor all of the notifications described above (e.g., text message, call,email, etc.) to a specialist involved in the clinical and/or researchtrial, a specialist who has actively turned on notifications forclinical trial recruitment, a researcher, a research principalinvestigator, an administrative assistant, the patient himself, or anyother suitable entity or individual.

4.8 Method—Aggregating Data S260

The method 200 can optionally include any number of sub-steps involvingthe aggregation of data involved in and/or generated during the method200, which can function to improve future iterations of the method 200(e.g., better match patients with a specialist, decrease time to treat apatient, increase sensitivity, increase specificity, etc.). Theaggregated data is preferably used in one or more analytics steps (e.g.,to refine a treatment option, make a recommendation for a drug orprocedure, etc.), but can additionally or alternatively be used for anyother suitable purpose.

In some variations, for instance the outcomes of the patients examinedduring the method 200 are recorded and correlated with theircorresponding data packets, which can be used to assess the success ofthe particular treatment options chosen and better inform treatmentoptions in future cases.

5. Variations

In one variation of the system 100, the system includes a router 110,which operates at a computing system at a 1^(st) point of care andreceives image data from an imaging modality. The router transmits theimage data to a remote computing system, wherein a series of algorithms(e.g., machine learning algorithms) are performed at the remotecomputing system, which determines a hypothesis for whether or not asuspected condition (e.g., ICH) is present based on the image dataand/or any associated metadata. Based on the determination, a contact isdetermined from a lookup table (e.g., in storage at the remote computingsystem), wherein the contact is notified at a user device (e.g.,personal device) and sent image data through a client applicationexecuting on the user device. One or more inputs from the contact at theapplication can be received at the remote computing system, which can beused to determine a treatment option (e.g., next point of care) for thepatient. However, the system and/or components thereof can be used inany other suitable manner.

In a first variation of the method 200, the method operates in parallelwith a standard radiology workflow, which can include any or all of: ata remote computing system (e.g., remote from the first point of care),receiving a set of images (e.g., of a brain of the patient), wherein theset of images is concurrently sent to the standard radiology workflowoperating in parallel with the method and automatically detecting acondition (e.g., ischemic core, early signs of ischemic core, etc.) fromthe set of images. Upon condition detection, the method can include anyor all of, automatically: determining a second specialist from thestandard radiology workflow, wherein the specialist is associated with asecond point of care; notifying the second specialist on a mobile deviceassociated with the second specialist before the radiologist notifiesthe first specialist; displaying a compressed version of the set ofimages on the mobile device; and initiating a pull of the patient (e.g.,from the 1^(st) point of care to the 2^(nd) point of care, from the1^(st) point of care to a clinical trial at a later date, as initiatedby a specialist at the 2^(nd) point of care, as initiated by aspecialist at the 1^(st) point of care, etc.).

In a specific example (e.g., as shown in FIG. 7 , as shown in FIG. 8 ),the method includes, at a remote computing system, receiving a set ofbrain images associated with the patient, wherein the set of brainimages is concurrently sent to a standard radiology workflow operatingin parallel with the method. In the standard radiology workflow, theradiologist analyzes the set of brain images and notifies a specialistbased on a visual assessment of the set of brain images at theworkstation, wherein the standard radiology workflow takes a firstamount of time. The method can then include detecting an ischemiccondition from the set of brain images, which includes any or all of:identifying an image dataset of a head from a brain scan; resizing eachof the set of images; producing a mirror image of each of the set ofimages; superimposing the image with its mirror image; registering theimage based on the superimposed image; detecting a potential ischemiccore through a segmentation process using a deep CNN; providing anotification (e.g., through texting) to a specialist associated with a2^(nd) point of care (e.g., different from the 1^(st) point of care,within the 1^(st) point of care, etc.); sending a compressed imagedataset to the specialist; displaying a high resolution image dataset ona workstation of the specialist; and triggering an action based on inputfrom the specialist (e.g., transfer of patient from the 1^(st) point ofcare to the 2^(nd) point of care, recommending the patient for aclinical trial, etc.). Within this variation and/or additionally oralternatively, the mirror image can be taken as an input afterregistration to the original image, wherein the original image isregistered (e.g., straightened) based on its mirror image, wherein aregistered mirror image of the registered image is then produced andused as an input.

In a second variation of the method 200, additional or alternative tothe first, the method includes: at a remote computing system (e.g.,remote from the first point of care), receiving a set of images (e.g.,of a brain of the patient), automatically detecting a condition (e.g.,ischemic core, early signs of ischemic core, etc.) from the set ofimages based on a deep learning model, determining a recipient (e.g., aspecialist at a 1^(st) point of care, a specialist at a 2^(nd) point ofcare, a research coordinator of a clinical trial, etc.); notifying therecipient on a mobile device associated with the recipient; optionallydisplaying a compressed version of the set of images on the mobiledevice; and receiving an input from the recipient.

In a set of specific examples, the method further includes establishingcommunication (e.g., texting, call, HIPAA-compliant texting,HIPAA-compliant calling, etc.) between recipients, such as between anyor all of: multiple healthcare workers (e.g., physicians, surgeons,etc.), multiple research coordinators (e.g., from the same clinicaltrial, from different clinical trials, etc.), a healthcare worker and aresearch coordinator (e.g., for the research coordinator to askquestions from the surgeon, as shown in FIG. 13 , etc.), a researchcoordinator and a patient (e.g., to submit a consent form to thepatient, to receive a consent form from the patient, etc.), a healthcareworker and a patient, and/or between any other suitable users andindividuals.

In a third variation of the method 200 (e.g., as shown in FIG. 9 , asshown in FIG. 11 , as performed in accordance with a system shown inFIG. 12 , etc.), additional or alternative to those described above, themethod functions to evaluate if a patient presenting with a potentialpathology qualifies for a clinical trial and if so, to alert (e.g.,automatically, in a time period shorter than a determination made by aradiologist in a standard radiology workflow, etc.) a researchcoordinator (e.g., principal investigator) associated with the clinicaltrial, wherein the method includes: receiving a data packet comprising aset of images (e.g., NCCT images of a brain of the patient) sampled atthe first point of care, wherein the data packet is optionallyconcurrently sent to the standard radiology workflow; segmenting theimages and comparing with set of clinical trial criteria (e.g.,inclusion criteria, exclusion criteria, etc.); and in an event that theimages satisfy the clinical trial criteria (e.g., according to a set ofthresholds), presenting a notification on a mobile device associatedwith the research coordinator (e.g., as shown in FIG. 11 ). If theresearch coordinator decides to include the patient in the clinicaltrial (e.g., based on the notification, based on a set of compressedimages sent to a user device of the research coordinator, based on acalculated parameter, based on a consensus reached by a clinical trialcommittee in communication with the research coordinator, etc.), theresearch coordinator or other user or entity can optionally transmit aconsent form to the patient (e.g., to a user device of the patient, to aworkstation associated with the 1^(st) point of care, to a workstationassociated with the 2^(nd) point of care, etc.) and/or to a healthcareworker (e.g., to a user device of the healthcare worker, to aworkstation of the healthcare worker, etc.), such as a surgeon,associated with the patient (e.g., via a client application executing ona user device of the research coordinator, from a remote computingsystem, etc.). Additionally or alternatively, the research coordinatorcan communicate (e.g., via a HIPAA-compliant messaging platformestablished through the client application, through a text messagingapplication, etc.) with a physician associated with the patient, and/orotherwise review and communicate information.

Additionally or alternatively, the method can include any or all of:providing a mobile image viewer at a client application with visibleprotected health information after secure login by the recipient;providing a patient status tracker to keep the recipient informed ofupdates to the patient; providing case volume information, which candetect and alert recipients about patients throughout the hub and spokenetwork that can benefit from a particular treatment (e.g.,neurosurgical treatment); providing bed capacity information to arecipient, which enables increased access to early surgical intervention(e.g., thereby leading to improved patient outcomes, decreased hospitallength of stay, decreased ventilator use, etc.); and/or any otherprocesses.

Additionally or alternatively, the method can include any other stepsperformed in any suitable order.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, wherein the method processes can beperformed in any suitable order, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for automatically detecting a potential ischemiccondition, the method comprising: receiving a first set of imagesassociated with a patient; producing a mirror image version of each ofthe set of images, thereby producing a mirrored set of images;overlaying the mirrored set of images with the first set of images,thereby producing a set of overlaid images; automatically detecting thepotential ischemic condition with a model, wherein the model receives asinput the first set of images and the set of overlaid images.
 2. Themethod of claim 1, further comprising, in response to detecting thepotential ischemic condition, automatically determining a specialistassociated with a treatment of a potential ischemic condition.
 3. Themethod of claim 2, further comprising notifying the specialist on adevice associated with the specialist.
 4. The method of claim 3, whereinthe device is at least one of a mobile user device and a workstation. 5.The method of claim 2, wherein the first set of images is taken at afirst point of care and wherein the specialist is associated with asecond point of care.
 6. The method of claim 1 wherein the model is amachine learning model.
 7. The method of claim 1, wherein the ischemiccondition is an ischemic core.
 8. The method of claim 1, furthercomprising, with the model, segmenting an ischemic region from the firstset of images, wherein the potential ischemic condition is determinedbased on the ischemic region.
 9. The method of claim 1, whereinautomatically detecting the potential ischemic condition supplements astandard radiology workflow.
 10. A method for automatically detecting apotential ischemic condition, the method comprising: receiving a firstset of images associated with a patient; processing the first set ofimages with a model, wherein processing the set of images comprisessegmenting an ischemic region corresponding to the ischemic condition,and wherein a set of inputs of the model comprises the first set ofimages and a mirrored version of the first set of images; andautomatically detecting the potential ischemic condition based on thesegmented ischemic region.
 11. The method of claim 10, wherein theischemic condition is an ischemic core.
 12. The method of claim 10,wherein the model is a machine learning model.
 13. The method of claim12, wherein the machine learning model is a deep learning model.
 14. Themethod of claim 10, further comprising, in response to detecting thepotential ischemic condition, automatically triggering the transmissionof a notification to a device.
 15. The method of claim 14, wherein thenotification comprises a compressed version of at least one of the firstset of images.
 16. The method of claim 15, wherein the at least one ofthe first set of images corresponds to a maximum cross section of theischemic region.
 17. The method of claim 14, wherein the device isassociated with a specialist at a first point of care.
 18. The method ofclaim 17, wherein the first set of images is taken at a second point ofcare remote from the first point of care.
 19. The method of claim 18,wherein the device is a mobile user device.
 20. The method of claim 10,wherein automatically detecting the potential ischemic conditionsupplements a standard radiology workflow.